Adversarial mimicry attacks against image splicing forensics: An approach for jointly hiding manipulations and creating false detections-Reference-Cited by-同舟云学术

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

Published:2024-03 Issue: Volume:179 Page:73-79
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Boato Giulia^ORCID,De Natale Francesco G.B.,De Stefano Gianluca,Pasquini Cecilia,Roli Fabio

Funder

Ministero dell’Istruzione, dell’Università e della Ricerca

Governo Italiano Ministero dell'Istruzione dell'Universita e della Ricerca

Defense Advanced Research Projects Agency

European Commission

Publisher

Elsevier BV

Reference26 articles.

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2. M. Huh, A. Liu, A. Owens, A.A. Efros, Fighting Fake News: Image Splice Detection via Learned Self-Consistency, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018.

3. A. Ghosh, Z. Zhong, T.E. Boult, M. Singh, SpliceRadar: A Learned Method For Blind Image Forensics, in: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019.

4. Wild patterns: Ten years after the rise of adversarial machine learning;Biggio;Pattern Recognit.,2018

5. A. Rozsa, T.E. Boult, Z. Zhong, Adversarial Attack on Deep Learning-Based Splice Localization, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW, 2020.

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