We introduce a new backdoor attack against a deep-learning video rebroadcast detection network. In addition to the difficulties of working with video signals rather than still images, injecting a backdoor into a deep learning model for rebroadcast detection presents the additional problem that the backdoor must survive the digital-to-analog and analog-to-digital conversion associated to video rebroadcast. To cope with this problem, we have built a backdoor attack that works by varying the average luminance of video frames according to a predesigned sinusoidal function. In this way, robustness against geometric transformation is automatically achieved, together with a good robustness against luminance transformations associated to display and recapture, like Gamma correction and white balance. Our experiments demonstrate the effectiveness of the proposed backdoor attack, especially when the attack is carried out by also corrupting the labels of the attacked training samples.

Bhalerao, A., Kallas, K., Tondi, B., Barni, M. (2019). Luminance-based video backdoor attack against anti-spoofing rebroadcast detection. In 2019 IEEE 21ST INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP 2019). New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/MMSP.2019.8901711].

Luminance-based video backdoor attack against anti-spoofing rebroadcast detection

Kallas K.;Tondi B.;Barni M.
2019-01-01

Abstract

We introduce a new backdoor attack against a deep-learning video rebroadcast detection network. In addition to the difficulties of working with video signals rather than still images, injecting a backdoor into a deep learning model for rebroadcast detection presents the additional problem that the backdoor must survive the digital-to-analog and analog-to-digital conversion associated to video rebroadcast. To cope with this problem, we have built a backdoor attack that works by varying the average luminance of video frames according to a predesigned sinusoidal function. In this way, robustness against geometric transformation is automatically achieved, together with a good robustness against luminance transformations associated to display and recapture, like Gamma correction and white balance. Our experiments demonstrate the effectiveness of the proposed backdoor attack, especially when the attack is carried out by also corrupting the labels of the attacked training samples.
2019
978-1-7281-1817-8
978-1-7281-1818-5
Bhalerao, A., Kallas, K., Tondi, B., Barni, M. (2019). Luminance-based video backdoor attack against anti-spoofing rebroadcast detection. In 2019 IEEE 21ST INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP 2019). New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/MMSP.2019.8901711].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1105749