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Adversarial mimicry attacks against image splicing forensics: An approach for jointly hiding manipulations and creating false detections

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posted on 2024-05-22, 12:24 authored by Giulia Boato, Francesco GB De Natale, Gianluca De StefanoGianluca De Stefano, Cecilia Pasquini, Fabio Roli
The term “mimicry attack” has been coined in computer security and used in adversarial machine learning: an attacker observes what a machine-learning system has learned and adjusts the malicious input so that it mimics a benign input. In this paper we extend this concept to image forensics, to allow an attacker modifying a manipulated image so that it appears pristine when analyzed by a target forensic detector. Recent work has shown that such attacks can be executed against detectors based on deep networks for hiding image tampering. We do more than that: our mimicry attack can force the target detector to identify arbitrary fictitious manipulations, while hiding the true ones. Accordingly, the user of the forensic detector is completely misled. From a methodological viewpoint, the proposed attack artificially alters the detector-specific intermediate representations according to the pixel distribution in the manipulated image, by applying a gradient-based optimization process. Experimental tests on different data sets and detectors demonstrate that our approach succeeds in jointly hiding manipulated areas and arbitrarily adding new ones, favorably comparing with the state-of-the-art in the first task.

History

Primary Research Area

  • Empirical and Behavioral Security

Journal

Pattern Recognition Letters

Volume

179

Page Range

73-79

Publisher

Elsevier

Open Access Type

  • Hybrid

Sub Type

  • Article

BibTeX

@article{Boato:De Natale:De Stefano:Pasquini:Roli:2024, title = "Adversarial mimicry attacks against image splicing forensics: An approach for jointly hiding manipulations and creating false detections", author = "Boato, Giulia" AND "De Natale, Francesco GB" AND "De Stefano, Gianluca" AND "Pasquini, Cecilia" AND "Roli, Fabio", year = 2024, month = 3, journal = "Pattern Recognition Letters", pages = "73--79", publisher = "Elsevier", issn = "0167-8655", doi = "10.1016/j.patrec.2024.01.023" }