  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%.
