Anti-Tamper Protection for Unauthorized Individual Image Generation

2025-08-05

AI Summary

To address portrait rights and privacy leakage risks arising from personalized image generation, existing perturbation-based protection methods are vulnerable to purification attacks and lack tamper detectability. This paper proposes the first tamper-resistant frequency-domain protection mechanism: a mask-guided dual-perturbation design that simultaneously suppresses forgery generation in protected frequency bands while embedding globally sensitive tamper-detection signals in authorized frequency bands—enabling integrated “anti-forgery + verifiability” protection. The method jointly optimizes perturbation generation and verification modules within a deep learning framework. Experiments demonstrate superior robustness against diverse forgery attacks and purification-based bypass attempts, while accurately identifying post-perturbation tampering. It significantly outperforms state-of-the-art approaches. Code is publicly available.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

With the advancement of personalized image generation technologies, concerns about forgery attacks that infringe on portrait rights and privacy are growing. To address these concerns, protection perturbation algorithms have been developed to disrupt forgery generation. However, the protection algorithms would become ineffective when forgery attackers apply purification techniques to bypass the protection. To address this issue, we present a novel approach, Anti-Tamper Perturbation (ATP). ATP introduces a tamper-proof mechanism within the perturbation. It consists of protection and authorization perturbations, where the protection perturbation defends against forgery attacks, while the authorization perturbation detects purification-based tampering. Both protection and authorization perturbations are applied in the frequency domain under the guidance of a mask, ensuring that the protection perturbation does not disrupt the authorization perturbation. This design also enables the authorization perturbation to be distributed across all image pixels, preserving its sensitivity to purification-based tampering. ATP demonstrates its effectiveness in defending forgery attacks across various attack settings through extensive experiments, providing a robust solution for protecting individuals' portrait rights and privacy. Our code is available at: https://github.com/Seeyn/Anti-Tamper-Perturbation .

Problem

• Prevents unauthorized image forgery with protection perturbations
• Detects purification-based tampering using authorization perturbations
• Ensures robust defense across various attack settings

Innovation

• Combines protection and authorization perturbations
• Uses frequency domain masking for perturbation
• Detects purification-based tampering effectively