How to build an AI warship
The government’s competition to design an artificially intelligent warship has just doled out £3 million to the next round of successful entrants.
Eight companies – CGI IT UK Ltd, Decision Lab, DIEM Analytics, Frazer Nash Consultancy, Montvieux Ltd, Nottingham Trent University, Rolls Royce and SeeByte Ltd – are splitting the pot.
The cash has been awarded to create the next generation of military technology, intended for service from 2030. The Intelligent Shipo competition is being run by the Defence and Security Accelerator (DASA) on behalf of the Defence Science and Technology Laboratory (Dstl).
The first round of funding was awarded in January 2020. Nine companies split a £1 million pot then, with the second-round of funding using up the remainder of the original budget. It has not been confirmed that the competition won’t be extended with further funding.
AI included in the second round includes machinery management and mission planning, execution and analysis.
Julia Tagg, Dstl project technical authority said, “The Intelligent Ship project aims to demonstrate ways of bringing together multiple AI applications to make collective decisions, with and without human operator judgement.
Complex data environments
“We hope that the use of AI in the future will lead to timely, more informed and trusted decision-making and planning, within complex operating and data environments. With applications for the Royal Navy and more broadly across defence, we are very excited to see what these phase 2 projects might bring.”
Rachel Solomons, DASA Delivery Manager said, “DASA is focused on finding innovation to benefit the defence and security of the UK.
“Artificial Intelligence and human-machine teaming are such innovations, and by taking this competition to phase 2 we hope to help find solutions that could make a real difference to future decision making in defence.”
Facebook Develops AI to Crackdown on Deepfakes
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’.