Recently, significant advancements have been made in face recognition technologies using Deep Neural Networks. As a result, companies such as Microsoft, Amazon, and Naver offer highly accurate commercial face recognition web services for diverse applications to meet the end-user needs. Naturally, however, such technologies are threatened persistently, as virtually any individual can quickly implement impersonation attacks. In particular, these attacks can be a significant threat for authentication and identification services, which heavily rely on their underlying face recognition technologies’ accuracy and robustness. Despite its gravity, the issue regarding deepfake abuse using commercial web APIs and their robustness has not yet been thoroughly investigated. This work provides a measurement study on the robustness of black-box commercial face recognition APIs against Deepfake Impersonation (DI) attacks using celebrity recognition APIs as an example case study. We use five deepfake datasets, two of which are created by us and planned to be released. More specifically, we measure attack performance based on two scenarios (targeted and non-targeted) and further analyze the differing system behaviors using fidelity, confidence, and similarity metrics. Accordingly, we demonstrate how vulnerable face recognition technologies from popular companies are to DI attack, achieving maximum success rates of 78.0% and 99.9% for targeted (i.e., precise match) and non-targeted (i.e., match with any celebrity) attacks, respectively. Moreover, we propose practical defense strategies to mitigate DI attacks, reducing the attack success rates to as low as 0% and 0.02% for targeted and non-targeted attacks, respectively. Read More
#fake, #image-recognitionDaily Archives: March 5, 2021
Tom Cruise deepfake creator says public shouldn’t be worried about ‘one-click fakes’
Weeks of work and a top impersonator were needed to make the viral clips
When a series of spookily convincing Tom Cruise deepfakes went viral on TikTok, some suggested it was a chilling sign of things to come — harbinger of an era where AI will let anyone make fake videos of anyone else. The video’s creator, though, Belgium VFX specialist Chris Ume, says this is far from the case. Speaking to The Verge about his viral clips, Ume stresses the amount of time and effort that went into making each deepfake, as well as the importance of working with a top-flight Tom Cruise impersonator, Miles Fisher.
“You can’t do it by just pressing a button,” says Ume. “That’s important, that’s a message I want to tell people.” Each clip took weeks of work, he says, using the open-source DeepFaceLab algorithm as well as established video editing tools. “By combining traditional CGI and VFX with deepfakes, it makes it better. I make sure you don’t see any of the glitches.” Read More
Self-supervised Pretraining of Visual Features in the Wild
Recently,self-supervised learning methods like MoCo [22], SimCLR [8], BYOL [20] and SwAV [7] have reduced the gap with supervised methods.These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model,a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy,surpassing the best self-supervised pretrained model by 1%and confirming that self-supervised learning works in areal world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving77.9% top-1 with access to only 10% of ImageNet. Read More
Rick's Cafe AI 