Learning to Immunize Images for Tamper Localization and Self-Recovery
This addresses the vulnerability of digital images to tampering for applications requiring integrity and authenticity, representing an incremental improvement over prior immunization methods.
The paper tackles the problem of protecting digital images from tampering attacks by introducing Imuge+, an enhanced image immunization scheme that uses an invertible neural network to jointly learn immunization and self-recovery, achieving accurate tamper localization and high-fidelity content recovery in real-world tests.
Digital images are vulnerable to nefarious tampering attacks such as content addition or removal that severely alter the original meaning. It is somehow like a person without protection that is open to various kinds of viruses. Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered. This paper presents Imuge+, an enhanced scheme for image immunization. By observing the invertible relationship between image immunization and the corresponding self-recovery, we employ an invertible neural network to jointly learn image immunization and recovery respectively in the forward and backward pass. We also introduce an efficient attack layer that involves both malicious tamper and benign image post-processing, where a novel distillation-based JPEG simulator is proposed for improved JPEG robustness. Our method achieves promising results in real-world tests where experiments show accurate tamper localization as well as high-fidelity content recovery. Additionally, we show superior performance on tamper localization compared to state-of-the-art schemes based on passive forensics.