SPN Dash - Fast Detection of Adversarial Attacks on Mobile via Sensor Pattern Noise Fingerprinting

A concerning weakness of deep neural networks is their susceptibility to adversarial attacks. While methods exist to detect these attacks, they incur significant drawbacks, ignoring external features which could aid in the task of attack detection. In this work, we propose SPN Dash, a method for det...

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Podrobná bibliografia
Vydané v:	2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) s. 1 - 6
Hlavní autori:	Nixon, Kent W., Mao, Jiachen, Shen, Juncheng, Yang, Huanrui, Li, Hai Helen, Chen, Yiran
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 01.11.2018
Predmet:	Detectors Feature extraction Image capture Image coding Image color analysis Image recognition Training
ISSN:	1558-2434
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Popis
Shrnutí:	A concerning weakness of deep neural networks is their susceptibility to adversarial attacks. While methods exist to detect these attacks, they incur significant drawbacks, ignoring external features which could aid in the task of attack detection. In this work, we propose SPN Dash, a method for detection of adversarial attacks based on integrity of sensor pattern noise embedded in submitted images. Through experiment, we show that our SPN Dash method is capable of detecting the addition of adversarial noise with up to 94% accuracy for images of size 256\times256. Analysis shows that SPN Dash is robust to image scaling techniques, as well as a small amount of image compression. This performance is on par with state of the art neural network-based detectors, while incurring an order of magnitude less computational and memory overhead.
ISSN:	1558-2434
DOI:	10.1145/3240765.3240851