Researchers Identify a Resilient Trait of Deepfakes That Could Aid Long-Term Detection

Since the earliest deepfake detection solutions began to emerge in 2018, the computer vision and security research sector has been seeking to define an essential characteristic of deepfake videos – signals that could prove resistant to improvements in popular facial synthesis technologies (such as autoencoder-based deepfake packages like DeepFaceLab and FaceSwap, and the use of Generative Adversarial Networks to recreate, simulate or alter human faces). Many of the'tells', such as lack of blinking, were made redundant by improvements in deepfakes, whereas the potential use of digital provenance techniques (such as the Adobe-led Content Authenticity Initiative) – including blockchain approaches and digital watermarking of potential source photos – either require sweeping and expensive changes to the existing body of available source images on the internet, or else would need a notable cooperative effort among nations and governments to create systems of invigilation and authentication. Therefore it would be very useful if a truly fundamental and resilient trait could be discerned in image and video content that features altered, invented, or identity-swapped human faces; a characteristic that could be inferred directly from falsified videos, without large-scale verification, cryptographic asset hashing, context-checking, plausibility evaluation, artifact-centric detection routines, or other burdensome approaches to deepfake detection. A new research collaboration between China and Australia believes that it has found this'holy grail', in the form of regularity disruption. The authors have devised a method of comparing the spatial integrity and temporal continuity of real videos against those that contain deepfaked content, and have found that any kind of deepfake interference disrupts the regularity of the image, however imperceptibly.