  Image forensics have attracted wide attention during the past decade. Though
many forensic methods have been proposed to identify image forgeries, most of
them are targeted ones, since their proposed features are highly dependent on
the image operation under investigation. The performance of the well-designed
features for detecting the targeted operation usually degrades significantly
for other operations. On the other hand, a wise attacker can perform
anti-forensics to fool the existing forensic methods, making countering
anti-forensics become an urgent need. In this paper, we try to find a universal
feature to detect various image processing and anti-forensic operations. Based
on our extensive experiments and analysis, we find that any image
processing/anti-forensic operations would inevitably modify many image pixels.
This would change some inherent statistics within original images, which is
similar to the case of steganography. Therefore, we model image
processing/anti-forensic operations as steganography problems, and propose a
detection strategy by applying steganalytic features. With some advanced
steganalytic features, we are able to detect various image operations and
further identify their types. In our experiments, we have tested several
steganalytic features on 11 different kinds of typical image processing
operations and 4 kinds of anti-forensic operations. The experimental results
have shown that the proposed strategy significantly outperforms the existing
forensic methods in both effectiveness and universality.
