In June 2019, a report revealed that a spy used an AI-generated profile picture to fool contacts in LinkedIn. As the researchers in the corresponding paper note, there’s a real need for tools like it - forged images might be abused for malicious purposes. In contrast to FaceShifter, Face X-Ray attempts to detect when a headshot might be a forgery. Extensive experiments demonstrate that the proposed framework significantly outperforms previous face swapping methods,” wrote the team. “The proposed framework shows superior performance in generating realistic face images given any face pairs without subject specific training. Moreover, even on “wild faces” scraped from the internet, the framework learned to recover anomaly regions - including glasses, shadow and reflection effects, and other uncommon occlusions - without relying on human-annotated data. The team says that in a qualitative test, FaceShifter preserved face shapes and faithfully respected the lighting and image resolution of targets.
Novelly, the generator incorporates what the researchers call Attentional Denormalization (AAD) layers, which adaptively learn where to integrate facial attributes, while a separate model - Heuristic Error Acknowledging Refinement Network (HEAR-Net) - leverages discrepancies between reconstructed images and their inputs to spot occlusions. FaceShifter boosts face swap fidelity with a generative adversarial network (GAN) - an AI model consisting of a generator that attempts to fool a discriminator into classifying synthetic samples as real-world samples - called Adaptive Embedding Integration Network (AEI-Net) that extracts attributes in various spatial resolutions. Apps like Reflect and FaceSwap purport to do this fairly accurately, but the coauthors of the Microsoft paper assert that they’re sensitive to posture and perspective variations. FaceShifter tackles the problem of replacing a person in a target image with that of another person in a source image, while at the same time preserving head pose, facial expression, lighting, color, intensity, background, and other attributes.
They say that both achieve industry-leading results compared with several baselines without sacrificing performance, and that they require substantially less data than previous approaches. In a pair of academic papers published by teams at Microsoft Research and Peking University, researchers propose FaceShifter and Face X-Ray, a framework for high-fidelity and occlusion-aware face swapping and a representation for detecting forged face images, respectively. State-of-the-art AI and machine learning algorithms can generate lifelike images of places and objects, but they’re also adept at swapping faces from one person to another - and of spotting sophisticated deepfakes. Microsoft Researchers propose face-swapping AI and face forgery detectorĪbove: Microsoft Research's FaceShift compared with existing methods.