ImageShield: A System for Image Tampering Detection, Localization, and Recovery

Abstract

Recent AI-based image editing tools have significantly increased the risk of spreading misinformation by enabling easy image manipulations. Most current image protection systems may either damage image quality by introducing visible artifacts and/or fail to defend against powerful AI-enabled attackers. Such attackers can, for example, extract authentication information embedded in an image, modify the image, and then compute and re-embed the compromised authentication information into the image. We propose ImageShield, a comprehensive solution for image protection. Unlike existing systems, ImageShield offers robust content authentication to verify image integrity, accurately identifies tampered regions, and restores a high-fidelity approximation of the original content. ImageShield’s core innovation is a dual-embedding strategy that resolves the conflicting requirements of authentication and recovery data. By using separate, specialized neural networks, it embeds cryptographically secure, low-capacity authentication data alongside high-capacity, error-tolerant recovery data, achieving a robust synergy that prior single-channel methods cannot. ImageShield is designed to withstand attacks from deep AI attackers without visually damaging the protected images. Our extensive experimental results with diverse datasets, complex attacks, and realistic image transformations show that ImageShield produces accurate authentication results and retains image quality. The results also show that ImageShield substantially outperforms the closest state-of-the-art system in the literature. Additionally, we conduct a security analysis of ImageShield using the STRIDE threat modeling framework, further validating its robustness against a wide range of attack vectors.