Combining Forensics and Privacy Requirements for Digital Images
This work addresses the trade-off between privacy and forensics in digital images, which is an incremental improvement for applications like secure image sharing.
The paper tackles the problem of balancing image forensics and privacy by proposing a selective encryption scheme that encrypts the most significant bits to hinder recognition while using the least significant bits for tampering detection. Results on the CASIA2 database show tampering detection accuracy above 80% for certain encryption levels and reduce class recognition accuracy to below 50%.
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%.