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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Vydáno v:2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) s. 1 - 6
Hlavní autoři: Nixon, Kent W., Mao, Jiachen, Shen, Juncheng, Yang, Huanrui, Li, Hai Helen, Chen, Yiran
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 01.11.2018
Témata:
Detectors
Feature extraction
Image capture
Image coding
Image color analysis
Image recognition
Training
ISSN:1558-2434
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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