  In recent years, convolutional neural networks (CNNs) have been widely used
by researchers to perform forensic tasks such as image tampering detection. At
the same time, adversarial attacks have been developed that are capable of
fooling CNN-based classifiers. Understanding the transferability of adversarial
attacks, i.e. an attacks ability to attack a different CNN than the one it was
trained against, has important implications for designing CNNs that are
resistant to attacks. While attacks on object recognition CNNs are believed to
be transferrable, recent work by Barni et al. has shown that attacks on
forensic CNNs have difficulty transferring to other CNN architectures or CNNs
trained using different datasets. In this paper, we demonstrate that
adversarial attacks on forensic CNNs are even less transferrable than
previously thought even between virtually identical CNN architectures! We show
that several common adversarial attacks against CNNs trained to identify image
manipulation fail to transfer to CNNs whose only difference is in the class
definitions (i.e. the same CNN architectures trained using the same data). We
note that all formulations of class definitions contain the unaltered class.
This has important implications for the future design of forensic CNNs that are
robust to adversarial and anti-forensic attacks.
