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Adversarial machine learning: The underrated threat of data poisoning

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AI adversarial model of panda and gibbon

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Most artificial intelligence researchers agree that one of the key concerns of machine learning is adversarial attacks, data manipulation techniques that cause trained models to behave in undesired ways. But dealing with adversarial attacks has become a sort of cat-and-mouse chase, where AI researchers develop new defense techniques and then find ways to circumvent them.

Among the hottest areas of research in adversarial attacks is computer vision, AI systems that process visual data. By adding an imperceptible layer of noise to images, attackers can fool machine learning algorithms to misclassify them. A proven defense method against adversarial attacks on computer vision systems is “randomized smoothing,” a series of training techniques that focus on making machine learning systems resilient against imperceptible perturbations. Randomized smoothing has become popular because it is applicable to deep learning models, which are especially efficient in performing computer vision tasks.

Above: Adversarial example: Adding an imperceptible layer of noise to this panda picture causes a convolutional neural network to mistake it for a gibbon.

Image Credit: TechTalks

But randomized smoothing is not perfect. And in a new paper accepted at this year’s Conference on Computer Vision and Pattern Recognition (CVPR), AI researchers at Tulane University, Lawrence Livermore National Laboratory, and IBM Research show that machine learning systems can fail against adversarial examples even if they have been trained with randomized smoothing techniques. Titled “How Robust are Randomized Smoothing based Defenses to Data Poisoning?,” the paper sheds light on previously overlooked aspects of adversarial machine learning.

Data poisoning and randomized smoothing

One of the known techniques to compromise machine learning systems is to target the data used to train the models. Called data poisoning, this technique involves an attacker inserting corrupt data in the training dataset to compromise a target machine learning model during training. Some data poisoning techniques aim to trigger a specific behavior in a computer vision system when it faces a specific pattern of pixels at inference time. For instance, in the following image, the machine learning model will tune its parameters to label any image with the purple logo as “dog.”

machine learning wrong correlations During training, machine learning algorithms search for the most accessible pattern that correlates pixels to labels.

Above: During training, machine learning algorithms search for the most accessible pattern that correlates pixels to labels.

Image Credit: TechTalks

Other data poisoning techniques aim to reduce the accuracy of a machine learning model on one or more output classes. In this case, the attacker would insert carefully crafted adversarial examples into the dataset used to train the model. These manipulated examples are virtually impossible to detect because their modifications are not visible to the human eye.

Research shows that computer vision systems trained on these examples would be vulnerable to adversarial attacks on manipulated images of the target class. But the AI community has come up with training methods that can make machine learning models robust against data poisoning.

“All previous data poisoning methods assume that the victim will use the standard training procedure of minimizing the empirical error on the training data,” Akshay Mehra, Ph.D. student at Tulane University and lead author of the paper, told TechTalks. “However, the adversarial robustness community has highlighted that minimizing the empirical error is not suitable for model training since models trained with it are vulnerable to adversarial attacks. Several works have been published that try to improve the adversarial robustness of the models. Of these works, training procedures that can produce certifiably robust models are of the most interest due to the adversarial robustness guarantees of the models, trained using these methods.”

Random smoothing is a technique that cancels out the effects of data poisoning by establishing an average certified radius (ACR) during the training of a machine learning model. If a trained computer vision model classifies an image correctly, then adversarial perturbations within the certified radius will not affect its accuracy. The larger the ACR, the harder it becomes to stage an adversarial attack against the machine learning model without making the adversarial noise visible to the human eye.

Experiments show that deep learning models trained with random smoothing techniques maintain their accuracy even if their training dataset contains poisoned examples.

smoothing makes machine learning models more robust

Above: Random smoothing makes machine learning models more robust to data poisoning techniques.

In their research, Mehra and his coauthors assumed that a victim has used random smoothing to make the target robust against adversarial attacks. “In our work, we explored three popular training procedures (Gaussian data augmentation, smooth adversarial training, and MACER) which have been shown to increase certified adversarial robustness of the models as measured by the state-of-the-art certification method based on randomized smoothing,” Mehra says.

Their findings show that even when trained with certified adversarial robustness techniques, machine learning models can be compromised through data poisoning.

Poisoning Against Certified Defenses and bilevel optimization

In their paper, the researchers introduce a new data poisoning method called Poisoning Against Certified Defenses (PACD). PACD uses a technique known as bilevel optimization, which achieves two goals: create poisoned data for models that have undergone robustness training, and pass the certification procedure. PACD produces clean adversarial examples, which means the perturbations are not visible to the human eye.

PACD poisoned images

Above: Poisoned data generated through the PACD method (even rows) are visually undistinguishable from their original versions (odd rows).

Image Credit: TechTalks

“A few previous works have shown the effectiveness of solving the bilevel optimization problem to achieve better poisoning data,” Mehra says. “The difference in the formulation of the attack in this work is that instead of using the poison data to reduce the model accuracy we are targeting certified adversarial robustness guarantees obtained from state-of-the-art certification procedure based on randomized smoothing.”

The bilevel optimization process takes a set of clean training examples and gradually adds noise to them until they reach a level that can circumvent the target training technique. The ingenuity behind this data poisoning technique is that researchers were able to create a machine learning algorithm that optimizes the adversarial noise for the specific type of robustness training method used in the target model. The algorithm that creates the adversarial example is called ApproxGrad, and it can be adjusted for different robustness training methods.

Once the target model is trained on the tainted dataset, its ACR will be reduced considerably, and it will be highly vulnerable to adversarial attacks.

pacd data poisoning schema

Above: Poisoning Against Certified Defenses generates poisoned data that have been optimized for specific adversarial robustness techniques.

Image Credit: TechTalks

“In our approach, we explicitly generated poison data that when used for training, will lead to models with low certified adversarial robustness,” Mehra says. “To do this we used the training procedures that produce models with high certified adversarial robustness as our lower-level problem. The attacker’s objective (upper-level problem) is to lower the guarantees produced by the certification procedure. By approximately solving this bilevel optimization problem we were able to generate poison data that could significantly hurt the certified adversarial robustness guarantees of the models. The lowered guarantees lead to a loss of trust in the model’s prediction at test-time.”

The researchers applied PACD to the MNIST and CIFAR datasets and tested it on neural networks trained with all three popular adversarial robustness techniques. In all cases, PACD data poisoning resulted in a considerable decrease in the average certified radius of the trained model, making it vulnerable to adversarial attacks.

Transfer learning on adversarial attacks

The AI researchers also tested to see whether a poisoned dataset targeted at one adversarial training technique would prove to be effective against others. Interestingly, their findings show that PACD transfers across different training techniques. For instance, even if a poisoned dataset has been optimized for gaussian data augmentation, it will still be effective on machine learning models that will go through the MACER and smooth adversarial training processes.

“We demonstrate, through transfer learning experiments, that the generated poison data works to reduce the certified adversarial robustness guarantees of models trained with different methods and also models with different architectures,” Mehra says.

But while PACD has proven to be effective, it comes with a few caveats. Adversarial attacks that assume full knowledge of the target model, including its architecture and weights, are called “white box attacks.” Adversarial attacks that only need access to the output of a machine learning model are “black box attacks.” PACD stands somewhere in between the two ends of the spectrum. The attacker needs to have some general knowledge of the target machine learning model before formulating the poisoned data.

“Our attack is a grey box attack since we are assuming knowledge of victim’s model architecture and training method,” Mehra says. “But we don’t assume knowledge of the particular weights of the network.”

Another problem with PACD is the cost of producing the poisoned dataset. ApproxGrad, the algorithm that generates the adversarial examples, becomes computationally expensive when applied to large machine learning models and complicated problems. In their experiments, the AI researchers focused on small convolutional neural networks trained to classify the MNIST and CIFAR-10 datasets, which contain no more than 60,000 training examples. In their paper, the researchers note, “For datasets like ImageNet where the optimization must be performed over a very large number of batches, obtaining the solution to bilevel problems becomes computationally hard. Due to this bottleneck we leave the problem of poisoning ImageNet for future work.”

ImageNet contains more than 14 million examples. A machine learning model that can perform well on the ImageNet dataset requires a convolutional neural network with dozens of layers and millions of parameters. Accordingly, creating PACD data would require large resources.

“Solving bilevel optimization problems can be computationally expensive, especially when using very large datasets and deep models,” Mehra says. “However, in our paper, we show that attacks generated against moderately deep models transfer well to much deeper models. It would be interesting to see if attacks generated against a portion of the large training data also work well on the entire training data.”

The future of adversarial attacks and data poisoning

Adversarial ML Threat Matrix

Above: The Adversarial Threat ML Matrix provides guidelines for finding vulnerabilities in the machine learning development and deployment pipeline.

Image Credit: TechTalks

Today, machine learning applications have created new and complex attack vectors in the millions of parameters of trained models and the numerical values of image pixels, audio samples, and text documents. Adversarial attacks are presenting new challenges for the cybersecurity community, whose tools and methods are centered on finding and fixing bugs in source code.

The PACD technique shows that poisoned data can render proven adversarial defense methods ineffective. Mehra and his coauthors warn that data quality is an underrated factor in assessing adversarial vulnerabilities and developing defenses.

For instance, a malicious actor can develop a tainted dataset and deploy it online for others to use in training their machine learning models. Alternatively, the attacker can insert poisoned examples into crowdsourced machine learning datasets. The adversarial perturbations are imperceptible to the human eye, which makes it extremely difficult to detect them. And automated tools that vet software security can’t detect them.

PACD has important implications for the machine learning community. Machine learning engineers should be more careful about the datasets they use to train their models and make sure the source is trustworthy. Organizations that curate datasets for machine learning training should be more careful about the provenance of their data. And companies such as Kaggle and GitHub that host datasets and machine learning models should start thinking about ways to verify the quality and security of their datasets.

We still don’t have complete tools to detect adversarial perturbations in training datasets. But securing the pipeline for accessing and managing machine learning training datasets can be a good first step in preventing the kind of data poisoning measures Mehra and his coauthors describe in their paper.

The Adversarial ML Threat Matrix, introduced last October, provides solid guidelines on finding and fixing possible holes in the training and deployment pipeline of machine learning models. But a lot more needs to be done. Another useful tool is a series of deep learning trust metrics developed by AI researchers at the University of Waterloo, which can find classes and areas where a computer vision system is underperforming and might be vulnerable to adversarial attacks.

“Through this work, we want to show that advances in certified adversarial robustness are dependent on the quality of the data used for training the models,” Mehra says. “Current methods for detecting data poisoning attacks may not be sufficient when attacker adds imperceptibly distorted data. We need more sophisticated methods to deal with this and is a direction for our future research.”

Ben Dickson is a software engineer and the founder of TechTalks, a blog that explores the ways technology is solving and creating problems.

This story originally appeared on Bdtechtalks.com. Copyright 2021

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The DeanBeat: The FOMO over the decline of triple-A games is unwarranted

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Public offerings of game companies took off in Q1 2021.

Did you miss GamesBeat Summit 2021? Watch on-demand here! 


“I must not fear.
Fear is the mind-killer.
Fear is the little-death that brings total obliteration.
I will face my fear.
I will permit it to pass over me and through me.
And when it has gone past I will turn the inner eye to see its path.
Where the fear has gone there will be nothing. Only I will remain.”

— Frank Herbert, the litany against fear in Dune.

We had another panic this week about the decline of triple-A video games, and it showed that we have a lot of fear of missing out as fans. But I think some of this fear is based on a misunderstanding about the industry’s unique status as both a business and an art form. Hardcore gamers like the art form, while business people want to get rich from it. They don’t always trust each other’s motivations.

Ubisoft’s chief financial officer Frederick Duguet set off the panic among hardcore gamers when he said in an earnings call that putting out three or four triple-A games is not “a proper indication of [Ubisoft’s] value-creation dynamics.” Instead, Ubisoft expects to make generate more revenue from free-to-play live-service games. And so it had announced The Division: Heartland, a free-to-play shooter. Many fans took Duguet’s comments to mean that Ubisoft is going to make fewer triple-A games. So Ubisoft’s PR department had to intercede with a clarification the next day.

“Our intention is to deliver a diverse line-up of games that players will love – across all platforms. We are excited to be investing more in free-to-play experiences, however we want to clarify that this does not mean reducing our AAA offering,” a spokesperson said in a statement. “Our aim is to continue to deliver premium experiences to players such as Far Cry 6, Rainbow Six Quarantine, Riders Republic and Skull & Bones to name a few while also expanding our free-to-play portfolio and strengthening our brands to reach even more players.”

In other words, Ubisoft reassured fans that it’s not taking away your triple-A games. By extension, I will argue that all of the fads of the moment — nonfungible tokens (NFTs), blockchain, augmented reality, free-to-play mobile games, live services on FIFA Soccer, esports, user-generated content, remakes and retro games — are not taking away from your triple-A games. As Jeff Grubb pointed out, they’re additive. The game industry is expected to hit $175.8 billion in 2021, according to game and entertainment data firm Newzoo. As an industry, it is taking away time from sports, movies, music, TV, and other hobbies.

Above: Public offerings of game companies took off in Q1 2021.

Image Credit: InvestGame

The industry has enough money to go around. Everything in games is getting funded. Investors are pouring money into public offerings, acquisitions, and game startup investments. Even indie game makers are benefiting from this, and they continue to be the creative heartbeat of the industry, supplying the innovative games like Hades that triple-A game companies aren’t making. The first quarter saw $39 billion invested into the game industry in 280 announced transactions, according to InvestGame. That quarterly amount was higher than $33 billion reported for all of 2020.

Will mobile games get more budgeted money? Yes. Mobile games are 51% of the market and are growing. PC and consoles games could actually shrink in 2021, based on delays shipping big games during the pandemic. That’s going to happen, as it’s easier to invest in mobile games and increasingly harder to invest in PC and console games, which are often delayed.

“That’s kind of the dirty little secret of the video game business is that it is a business, after all, and we need to do, we need to create an audience, we need to create a revenue stream the cash flow in order to continue to create new and exciting games for people to play,” said Shawn Layden, former chairman of Sony Worldwide Game Studios, said at our recent GamesBeat Summit 2021 event.

You may not trust my answer here, but this is a good thing. The strategy that I see everybody pursuing right now makes perfect sense, and it will be good for all of games.

Why this is good news

First, mobile and free-to-play triple-A games are expanding the market. They are the tip of the spear when it comes to penetrating new markets and convincing people that games are a good use of their time. We’re at 3 billion gamers and growing, but not everybody on the planet is a gamer yet. By making the price of games more accessible, we enable games to reach more people. Those people will pick up the habit. They will find the new point of entry, and they will become gamers, hopefully for life. They will also keep playing these accessible and less time-consuming games even in periods of life when they’re busier, like when they have kids or have to study a lot or have to pour a lot of energy into work.

The key is that they are the point of entry into the vastness of games. Consider Call of Duty. Bobby Kotick, CEO of Activision Blizzard, had some foresight in getting three major game studios to make Call of Duty games in parallel, so that a new one could be launched every year without a sacrifice in the quality of the triple-A game. That wasn’t an easy process, and many accused Kotick of wrecking the franchise by making it too frequent. But the developers didn’t run into creative exhaustion. They converted players into wanting to play Call of Duty every year.

Now nine studios or so are working on Call of Duty. That allowed Activision Blizzard to add the free-to-play games Call of Duty: Mobile and Call of Duty: Warzone. These became the new points of entry for Call of Duty. Call of Duty also went cross-platform so you could play with friends wherever they were. You could start at the top of the funnel, playing for free. Within Warzone, all you had to do to upgrade to the $60 premium game was click a few buttons. Analyst Michael Pachter of Wedbush Securities estimates that Call of Duty premium game sales went up from around 25 million a year to 35 million a year. The result was record performance for Activision Blizzard in 2020. Now people play Call of Duty every year. And if you follow what Kotick said at our GamesBeat Summit 2021 event, increasing the share spent in the day by creating some kind of Call of Duty metaverse is probably the next goal.

Kotick said that the 10,000-person company now needs at least 2,000 more people to meet its production obligations. It’s making triple-A games like Diablo 4, but it is also making the free-to-play Diablo Immortal game for mobile. Do you see the pattern? Kotick is using the same strategy of Call of Duty with Diablo. Mobile and free-to-play games are the onramps to the franchise and you can expect to see Activision Blizzard execute on the same strategy for every major franchise.

No fear

Skull & Bones is looking awesome.

Above: Skull & Bones is coming one of these days from Ubisoft.

Image Credit: Ubisoft

The financial success of Call of Duty and Activision Blizzard isn’t lost on Electronic Arts, which is making a mobile game based on Battlefield. That will be the onramp for Battlefield VI, the triple-A game that is in production. EA has a mobile Apex Legends game that will be the onramp for the free-to-play Apex Legends, and maybe Respawn will fill out the roster with a triple-A Apex Legends (or maybe Titanfall) premium game.

With Ubisoft, the free-to-play The Division: The Heartland can be an onramp to The Division or The Division 2 games. And so on. These efforts are not going to cannibalize each other, in my opinion. They are going to make it more likely that players will become hobbyists. The hobby will not just be games. It will be more specific than that. The hobby will become Call of Duty, or Diablo, or Apex Legends, or The Division. These franchises will command all of our time, and people will constantly cycle through them from the top of the funnel to the bottom.

On our GamesBeat Summit panel, Layden was more focused on Sony’s own specific challenges. But he was right in that platform owners — and by extension the whole game industry — has the responsibility of expanding the market. The lower the price point, the lower risk it is for the industry. Hollywood, by contrast, has been slow to lower the ticket prices of movies. In fact, it raised them just in time for the pandemic. It’s no surprise that streaming movie services took off during the pandemic because they were cheaper. The price spectrum of games captures all the right players.

Hardcore gamers should also be aware that what they want to play isn’t what everyone wants to play. As the game industry expands out of its ghetto of 200 million or 300 million gamers, it will have to serve more diverse content than it ever has, to capture people like older players, international players in emerging markets and different cultures, and women. As it expands to mobile and free-to-play games, the industry should remember that it shouldn’t make just the same old franchises for the new players.

And as everybody becomes a gamer, the game market becomes bigger, the opportunity for each game is higher, and we will get better games of all kinds as a result — including better triple-A games.

The goose and the eggs

Dean Takahashi moderates a new IP panel with Shawn Layden, Ante Odic, and Marty O'Donnell.

Above: Dean Takahashi moderates a new IP panel with Shawn Layden, Ante Odic, and Marty O’Donnell.

Image Credit: GamesBeat

Layden, who had to oversee 13 first-party game studios for the PlayStation business, said that churning out sequels and providing fan service on important franchises is a necessary part of the business. But eventually, everyone comes around to realize the importance of doing original games.

“If we continue to make the same type of game over and over again, we will continue to appeal to the same audience we already have over and over again. We won’t be able to break out gaming into into a wider and larger business. We talk a lot about how video game business is the largest entertainment business in the world. But we really don’t punch above our weight when it comes to society and culture. And I think that’s because we don’t bring a diverse enough audience into enjoying gaming. And that’s why original intellectual property is important.”

Layden knows that going to a board of directors and pitching them a game that will cost $280 million to make over five years isn’t easy. That is a difficult pitch for anybody to make, no matter who you are. But those kinds of bets have to be made.

“It’s definitely problematic that the budgets have skyrocketed,” said one executive who participated in a secret roundtable at GamesBeat Summit 2021. “On the other end of the spectrum, that’s defensibility if you’ve got 10 million people playing every month. There are precious few that can assemble the budgets have the IP, have the distribution network, and the global brands to be able to compete in a market where people’s time is scarce.”

Grand Theft Auto Online: Arena Wars.

Above: Grand Theft Auto Online: Arena Wars.

Image Credit: Rockstar Games

A game like Grand Theft Auto V can sell 150 million units — a number that wasn’t possible more than a decade ago. So the upside is tremendous, and these franchises once established can give birth to live services, media spinoffs in adjacent entertainment markets, and high-margin mobile opportunities. The upside to that initial $280 million investment is tens of billions in additional market capitalization for the company that achieves it.

“If you perform at the highest level, and you have structural competitive advantages, it might actually be a virtue for the platform players at the top of the ecosystem to spend that much and deliver something that is polished and really, truly, triple-A,” he said. “You get a disproportionate share of a much, much bigger pie.”

Also on that panel was Ante Odic, senior vice president of product at Outfit7, the maker of the Talking Tom series and other games that have been downloaded 15 billion times. Even Outfit7 is investing to find the next Talking Tom as it knows that new IP is so critical. And just because it is investing in Talking Tom doesn’t mean that it isn’t investing in new IP. It’s not a zero-sum game.

“We have a wide audience,” Odic said. “But we want to go even wider.”

Marty O’Donnell, cofounder of Highwire Games and a former leader at Bungie, noted how the creative team wanted to move on from the successful Halo franchise to something new, so much so that they eventually spun Bungie out of Microsoft to be able to reach that aim.

“We wanted to do something new,” O’Donnell said. He reminded us of the fairy tale about the goose that laid the golden eggs. The important thing wasn’t the golden eggs. It was the goose. You don’t want to kill the goose laying the golden eggs, O’Donnell said.

“My slogan is be nice to the goose. And the goose is the team that lays the golden egg,” he said. “And being nice to the golden egg means you’re just going to make sequels that that are dead. But if you’re nice to the team that makes the lays the golden egg, that’s the only way to get really good new golden eggs. Certainly you don’t want to stab the goose and try to cut it open. But all I would ask for for publishers and developers is be nice to the goose because that’s how you’re going to get more eggs.”

A beautiful industry structure

DreamHaven is the new game company started by Mike and Amy Morhaime.

Above: DreamHaven is the new game company started by Mike and Amy Morhaime.

Image Credit: DreamHaven

And remember, if one company retreats from triple-A games, another may attack that opportunity. If Sony were to bail out of triple-A original games and shirk its responsibility, only to focus on sequels and free-to-play low-hanging fruit, it would lose its triple-A creators. They would go to another company like Nintendo or Microsoft or Epic Games or Valve or Ubisoft or Electronic Arts ….You get the point.

They could also seek creative freedom in indie games or start a new triple-A studio. That sort of thing is happening, as Harold Ryan has multiple triple-A games going at Probably Monsters. If Riot Games gets a little sleepy at innovation, the former Riot veterans at Theorycraft Games, which raised $37 million, or the scrappy ex-Riot team at Hidden Leaf Games will be happy to pick up the mantle and hire the Riot leaders who prefer to work on groundbreaking titles.

As I mentioned, a record amount of money is available to the game industry’s creators at all levels, from the newly minted public company Roblox that is worth $39.6 billion to Animoca Brands that has raised $88 billion at a $1 billion valuation to make NFT games to DreamHaven Games, founded by former Blizzard president Mike Morhaime and Amy Morhaime. The game industry has enough money pouring in at once to fund everything that it needs and to make every game that we want. It has never been like this before.

For gamers, don’t worry, be happy. And for game developers, heed what Layden said. “Find the best risks and take them. If you stay the course and keep true to the vision, you will be more delighted with the outcome.”

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GitHub now lets all developers upload videos to demo bugs and features

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GitHub now lets all developers upload videos to demo bugs and features

Join Transform 2021 this July 12-16. Register for the AI event of the year.


GitHub has officially opened up video uploads five months after launching in beta, allowing all developers to include .mp4 or .mov files directly in pull requests, discussions, issues, comments, and more.

The feature is designed to help developers visually demonstrate to project maintainers the steps they went through when they encountered a bug, for example, or illustrate what a major new code change achieves in terms of functionality.

So rather than having to follow detailed step-by-step textual instructions which may be ambiguous or unclear, it’s now easier to see exactly what’s happening at the other end first-hand and should go some way toward avoiding time-consuming back-and-forth written discussions. This could also be used in conjunction with a voice track with a narrator explaining the on-screen actions.

Above: Video in GitHub

It’s worth noting that with this launch, GitHub also now fully supports video uploads from within its mobile app.

ezgif.com gif maker 2

Above: Uploading video to GitHub via mobile app

Seeing is believing

Native video upload support helps bypass the cumbersome alternative involving recording and uploading a video to a third-party platform, then sharing a link. On that note, GitHub actually doesn’t yet support video unfurling from shared links, but that is something it said that it’s working on, alongside enabling video annotations for specific pieces of code.

At a time when the world has had to adapt to remote work and collaboration, learning to embrace asynchronous communication is one of the fundamental factors for distributed teams to succeed — recorded video plays a big part in enabling this.

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Warhammer III hands-on — A journey into the Realm of Chaos

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Warhammer III hands-on -- A journey into the Realm of Chaos

Did you miss GamesBeat Summit 2021? Watch on-demand here! 


Sega Europe’s The Creative Assembly studio showed off a demo of Total War: Warhammer III at a press event, and I got to go hands-on with the game in a battle set in the Realm of Chaos.

Being launched later on this year in partnership with franchise owner Games Workshop, Warhammer III the latest in the Total War series. The franchise has sold more than 34.3 million copies to date. The Total War: Warhammer spinoff is a cataclysmic conflict between demonic powers and the sentinels of the mortal world. I played the first two games, and many others, in the Total War series. This game brings the Warhammer trilogy to its conclusion.

The Creative Assembly has been making Total War strategy games for more than two decades. Most of these have focused on historical wars; until recently, when they’ve expanded into myths such as Total War: Three Kingdoms and fantasy with the Warhammer titles. In a Total War strategy game, you move armies around on a strategic map and fight in a 3D real-time battle when they meet on the battlefield.

In Total War: Warhammer III, each choice the player makes will shape the conflict to come. You’ll explore the mysterious Lands of the East to the demon-infested Realms of Chaos.

“Warhammer III is of course the concluding chapter in the series and we’re planning on going out with a bang,” said Al Bickham, the development communications manager for The Creative Assembly, at a press event. “We’ve crafted a huge arching narrative which ties the trilogy together. There are going to be more playable races out of the box than the previous two games. And it’s all set across a hyper-detailed campaign map which begins at the very fringes of Warhammer lands and takes you deep into the mind-bending horrors of the four Realms of Chaos.”

The game will have iconic races from the World of Warhammer Fantasy Battles, including the video game debut of Kislev and Cathay alongside the factions of Chaos — Khorne, Nurgle, Slaanesh, and Tzeentch. This means players will wage war with the most diverse array of legendary heroes, gargantuan monsters, flying creatures, and magical powers.

Embarking on a new grand campaign, you will be tasked with saving or exploiting the power of a dying god. Each race offers a unique journey through the nightmarish Chaos Realm. The endgame will determine the fate of the world.

The Survival Battle

Above: Everything looks so orderly at the beginning of the Survival Battle in Warhammer III.

Image Credit: Sega/Creative Assembly

The Creative Assembly used the Parsec to let me play a sample Survival Battle, where your goal is to attack into the Realm of Chaos and take objectives and fend off the demon hordes. It’s a new kind of narrated battle that is fresh to the franchise. They’re like boss battles in Warhammer III, and they trigger after you reach key points in the game’s narrative.

“We want the [Survival Battle] to feel epic, really memorable, and full of decisive moments in the course of your campaign,” Bickham said.

My faction was the Kislev, an Eastern human faction that resembles the Russian Cossacks. And I had to take a number of victory locations within the a bloody fortress called the Brass Citadel.

The faction leader, Tzarina Katarin (the Ice Queen of Kislev) has taken her loyal forces into the Realm of Chaos. Khorne, the Chaos God of rage and war, sends a legion of demons to destroy the trespassers. The Kislev forces have been detailed for the first time in the series. Katarin is an Ice Witch with magical powers to both rally her troops and strike fear in the hearts of demons.

I wasn’t exactly impressed with the forces I got in the battle. There were some excellent sword troops, but I only have five companies of them in a place where I had to defend against attacks coming from all directions. I had twice as many archers and a few archer cavalry units.

The Realm of Chaos, of course, is a bad place. It has plenty of blood-red backdrops and one of its decorations is an actual fountain of blood. The four Ruinous Powers rule over this place, ever seeking to slip their bonds and engulf the world in a tide of daemonic corruption. Nurgle, the plague god; Slaanesh, the lord of excess; Tzeentch, the changer of ways; and Khorne, the god of blood and slaughter.

My troops had to fight uphill and sweep some light demon units from the top of a ridge. That was easy enough, and I claimed a victory point in doing so. That allowed me to draw reinforcements from another realm to strengthen my army. But then I was attacked from four directions. At least I was defending a hill, but I had a hard time figuring out where to place my five sword troops, as they were the best units to stave off attacks.

chaos 5

Above: My soldiers are devolving into chaos in Warhammer III.

Image Credit: Sega/Creative Assembly

The cavalry was useful in taking down wolf-borne demons from the enemy, but it wasn’t useful in charging headlong into enemy lines. Rather, it was better to use them to harass the enemy with missile fire from a distance. But I didn’t have nearly enough units to form a full line of defense in all directions. The result was, you guessed it, chaos.

But I tried to survive. One of the goals was to earn a battle currency called “supplies,” which allowed me to build towers and barricades. It also let me recruit new warriors, upgrade my existing units, and bring on reinforcements. Being new to the game, I couldn’t figure out how much to spend on each kind of task. I found I could build barricades and get reinforcements, but I didn’t have enough supplies to build towers, and that meant the hordes of Chaos were going to charge me without being harassed. You generate more supplies by capturing victory points or killing enemies.

Had I looked more, I would have seen that I could have used The Lore of Ice, or ice-themed spells that would slow down the enemy and help my soldiers thin their ranks as they tried to attack. There were six different spells altogether. I also could have used the Elemental Bear, a huge monster on my side, and some of the bear cavalry for the faction. Sadly they were nowhere to be found in my playthrough.

Still, after a few battle restarts, I was able to survive the first wave of attacks and open up a new part of the Brass Citadel, which was circular with a big pit in the middle. Once again, I was forced to divide my forces and try to hold off larger numbers of enemies coming from all sides. It wasn’t pretty.

I didn’t get near the goal of the battle, to fight Khorne’s champion, an Exalted Greater Demon, in a final struggle. It was a very difficult battle, but I enjoyed the idea of being assaulted by endless hordes and figuring out how to stay alive when you’re vastly outnumbered. This is a difficult mode when it comes to figuring out where to throw your troops and when. But it adds some excitement to the pressure that you feel when you have to make decisions quickly to head off disaster.

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