Oct 15, 2020

CART AI simulation mimics nation-state cyberattacks

Paddy Smith
2 min
ai hacking
Boston-based FireCompass harnesses the power of AI to simulate thousands of hackers trying to breach an organisation’s cybersecurity...

With a hack executed every 39 seconds, companies security testing is paramount. But, according to the inventors of Continuous Automated Red Teaming (CART), their efforts fall short against real-world scenarios.

FireCompass, based in Boston, US, has developed CART to simulate thousands of hackers attempting a broad attack on an organisation. The team claims that scans that once took months can now be completed in a matter of days.

Its solution, which harnesses the power of artificial intelligence and SaaS, runs continuously without software installation, hardware or additional employees, automatically scanning the ever-changing digital attack surface of a company, including exposed databases, cloud buckets, code leaks, exposed credentials, risky cloud assets and open ports. It then launches multi-stage attacks to find attack paths missed by conventional cybersecurity tools.

Bikash Barai, co-founder of FireCompass said, "Organizations typically conduct security testing only a few times a year on a partial list of online assets, excluding shadow IT unknown to security teams. Meanwhile, hackers are always attempting attacks on the entirety of their assets. At FireCompass, our vision is to make Continuous Automated Red Teaming (CART) available to all so that organizations can discover and test all their assets at all times – just like real attackers do."

One risk manager at Sprint, a division of T-Mobile, said, "To our surprise, FireCompass has exceeded our expectations. The tool has demonstrated reliability in the findings, and FireCompass has proven to be a valuable service provider."

Firecompass was founded by industry veterans Barai, Nilanjan De and Priyanka Aash, who have broken cybersecurity giants including McAfee, Microsoft Bit Locker, Sophos and AVG.

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Jun 17, 2021

Facebook Develops AI to Crackdown on Deepfakes

Facebook
MSU
AI
Deepfakes
3 min
Social media giant, Facebook, has developed artificial intelligence that can supposedly identify and reverse-engineer deepfake images

In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale? 

 

Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.” 

 

The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with. 

Why Reverse Engineering? 

Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient. 

 

First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source. 

 

How It Works 

Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.

 

To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters. 

 

What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’. 

 

What Did They Find? 

‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising. 


Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.

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