Deepfake Detection

Detect AI-Generated models like Dall-E, Stable Diffusion, Mid Journey are extraordinary tools for content creation without being a graphic expert. The high accuracy in creating realistic face and full body figures will definitely pose challenges to detection solutions. Sensity can detect diffusion models creation with the 95.8% accuracy.

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.

AI-generated voiceshave become virtually indistinguishable from genuine human voices. These tools are exploited for deception during online communication, voice impersonation, and for automating the spread of misinformation, social engineering, and cyberattacks. Zero-shot machine-generated voice detection is achieved by assessing the probability that segments of audio are produced by popular AI voice services. This detector remains robust even when the content is edited by human operators, including alterations in wording, punctuation, and even mispronunciations.