Adversarial Machine Learning Against Digital Watermarking. Quiring, E. & Rieck, K. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 519-523, Sep., 2018.
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Paper doi abstract bibtex
Paper doi abstract bibtex Machine learning and digital watermarking are independent research areas. Their methods, however, are vulnerable to similar attacks if operated in an adversarial environment. Recent research has thus started to bring both fields together by introducing a unified view for black-box attacks and defenses between learning and watermarking methods. In this paper, we extend this work and examine a novel black-box attack against digital watermarking based on concepts from adversarial learning. With a set of marked images, we let a neural network approximate the watermark detection and use this network to remove the watermark. The attack does not require knowledge of the watermarking scheme.
@InProceedings{8553343,
author = {E. Quiring and K. Rieck},
booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
title = {Adversarial Machine Learning Against Digital Watermarking},
year = {2018},
pages = {519-523},
abstract = {Machine learning and digital watermarking are independent research areas. Their methods, however, are vulnerable to similar attacks if operated in an adversarial environment. Recent research has thus started to bring both fields together by introducing a unified view for black-box attacks and defenses between learning and watermarking methods. In this paper, we extend this work and examine a novel black-box attack against digital watermarking based on concepts from adversarial learning. With a set of marked images, we let a neural network approximate the watermark detection and use this network to remove the watermark. The attack does not require knowledge of the watermarking scheme.},
keywords = {cryptography;image watermarking;learning (artificial intelligence);neural nets;black-box attacks;watermark detection;watermarking scheme;adversarial environment;digital watermarking methods;adversarial machine learning;neural network;Watermarking;Machine learning;Detectors;Neural networks;Signal processing;Computational modeling;Media;Digital Watermarking;Adversarial Examples},
doi = {10.23919/EUSIPCO.2018.8553343},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438020.pdf},
}Downloads: 0
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