RDIAS: Robust and Decentralized Image Authentication System

Recent AI tools can subtly manipulate images, eroding users' trust in the authenticity of images they see on their displays. Current image authentication methods either detect artifacts that may result from manipulations or attach hashes of images as metadata for users to verify. The efficacy of the first approach is rapidly deteriorating with the continuous improvements in AI tools, leading to missing many serious manipulations. Hashes become invalid once images are subjected to any processing, such as re-sizing and transcoding. This makes the second approach impractical as most platforms, e.g., Facebook and X, perform several legitimate operations on images. Further, most platforms remove the metadata attached to images. We propose RDIAS, a robust and practical image authentication system. RDIAS securely embeds representative fingerprints into images without damaging their visual quality. We design these fingerprints to robustly detect malicious manipulations, e.g., adding/removing objects, while tolerating legitimate operations, e.g., image resizing and transcoding. Rigorous evaluation of RDIAS with diverse image datasets and realistic manipulations conducted by human subjects utilizing AI tools shows its high accuracy and efficiency. For example, RDIAS detects DeepFake manipulations that change facial features/expressions with an accuracy of 99%. The results also show that RDIAS preserves image quality and verifies authenticity in real time.