Paper Info
Title
Image counter-forensics based on feature injection
Abstract
Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image (x) over tilde, perceptually similar to x, whose feature f((x) over tilde) is as close as possible to f (y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Phi(z) = vertical bar f (z) f (y) vertical bar through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.
Year
DOI
Venue
2014
10.1117/12.2042234
Proceedings of SPIE
Keywords
Field
DocType
image forensics,counter-forensics,feature injection,feature decomposition,constrained minimization,gradient descent,numerical computation,bisection method
Computer vision,Bisection method,Gradient descent,Feature detection (computer vision),Feature (computer vision),Iterative method,Computer science,Minification,Artificial intelligence,Pixel,Detector
Conference
Volume
ISSN
Citations 
9028
0277-786X
3
PageRank 
References 
Authors
0.38
21
6
Name
Order
Citations
PageRank
massimo iuliani130.38
s rossetto230.38
Tiziano Bianchi3100362.55
Alessia De Rosa431220.66
Alessandro Piva52231157.21
M. Barni63091246.21