Learning to Immunize Images for Tamper Localization and Self-Recovery

Area of Science:

• Computer Science
• Image Processing
• Cybersecurity

Background:

• Digital images are susceptible to malicious tampering, altering their original meaning.
• Existing image immunization methods offer protection but lack robust recovery capabilities.
• The need for secure image authentication and content integrity is critical.

Purpose of the Study:

• To present Imuge+, an enhanced scheme for robust image immunization and content recovery.
• To develop a method that can accurately locate tampered image regions.
• To achieve high-fidelity recovery of original image content post-tampering.

Main Methods:

• Employed an invertible neural network to jointly learn image immunization and recovery.
• Utilized a forward pass for immunization and a backward pass for recovery.
• Introduced an efficient attack layer with a novel distillation-based JPEG simulator for robustness against post-processing.

Main Results:

• Achieved accurate tamper localization in real-world tests.
• Demonstrated high-fidelity content recovery of tampered images.
• Showcased superior tamper localization performance compared to state-of-the-art passive forensics schemes.

Conclusions:

• Imuge+ offers a robust solution for protecting digital images against malicious tampering.
• The proposed invertible neural network approach effectively balances immunization and recovery.
• The method provides accurate tamper detection and high-quality content restoration, enhancing image security.