Forensic Deepfake Detection

Detection of face swap and face reenactment

Deepfakes are used in realtime by fraudsters to impersonate their victims in the case of identity theft. Face swap and face reenactment are similar techniques where a target victim's face is either swapped onto the attacker's head, or controlled by the attacker's head movements. Similarly, deepfakes are also used to generate thousands of fake personas that can move and talk, with the goal to spoof automated KYC processes by brute force. Deepfake detection uses the latest advances in deep learning and image forensics to determine whether a person in the video is genuine.

GAN detection

Synthetic identities are commonly generated in form of images by GANs (Generative Adversarial Networks). GANs used as fake personas and bot accounts on social media and dating sites. Sensity's detector are trained on millions of GAN-generated images found online under various conditions of compression, cropping, and photo filters. They are trained to spot artefacts and high-frequency signals characteristics of AI-generated images, which natural photos are unlikely to possess.

Detection of face morphing

Morphing attacks pose a serious threat to face recognition systems, especially during the verification of passports and IDs. With minimal changes on the passport photo morphed to look more like them, a fraudster may spoof face matching and bypass an identity verification system. The detection of face morphing is based on a mix of technologies such as deep learning, computer vision and digital forensics.