Combining Forensics and Privacy Requirements for Digital Images

TL;DR

This paper explores a selective encryption method for digital images that balances privacy and forensic analysis, encrypting specific bits to hinder recognition while preserving tampering detection capabilities.

Contribution

It introduces a novel selective encryption scheme that encrypts the most significant bits of image pixels to enhance privacy without compromising forensic tampering detection.

Findings

Tampering detection accuracy exceeds 80% for s in {3..5} using SRMQ1 features.

Class recognition accuracy drops below 50% with CNN, ensuring privacy.

Effective trade-off demonstrated on CASIA2 database.

Abstract

This paper proposes to study the impact of image selective encryption on both forensics and privacy preserving mechanisms. The proposed selective encryption scheme works independently on each bitplane by encrypting the s most significant bits of each pixel. We show that this mechanism can be used to increase privacy by mitigating image recognition tasks. In order to guarantee a trade-off between forensics analysis and privacy, the signal of interest used for forensics purposes is extracted from the 8--s least significant bits of the protected image. We show on the CASIA2 database that good tampering detection capabilities can be achieved for s $\in$ {3,. .. , 5} with an accuracy above 80% using SRMQ1 features, while preventing class recognition tasks using CNN with an accuracy smaller than 50%.