Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks
Anmin Fu, Fanyu Meng, Huaibing Peng, Hua Ma, Zhi Zhang, Yifeng Zheng, Willy Susilo, Yansong Gao

TL;DR
UniGuard is a unified online detection framework that identifies adversarial examples and backdoor attacks by analyzing the propagation trajectories of inputs through deep learning models, using LSTM and spectrum transformation.
Contribution
It introduces a novel trajectory-based detection method that simultaneously addresses adversarial and backdoor attacks during run-time, outperforming state-of-the-art methods.
Findings
Effective across image, text, and audio modalities
Outperforms SOTA methods in diverse attack scenarios
Detects partial backdoors and dynamic triggers successfully
Abstract
The proposed UniGuard is the first unified online detection framework capable of simultaneously addressing adversarial examples and backdoor attacks. UniGuard builds upon two key insights: first, both AE and backdoor attacks have to compromise the inference phase, making it possible to tackle them simultaneously during run-time via online detection. Second, an adversarial input, whether a perturbed sample in AE attacks or a trigger-carrying sample in backdoor attacks, exhibits distinctive trajectory signatures from a benign sample as it propagates through the layers of a DL model in forward inference. The propagation trajectory of the adversarial sample must deviate from that of its benign counterpart; otherwise, the adversarial objective cannot be fulfilled. Detecting these trajectory signatures is inherently challenging due to their subtlety; UniGuard overcomes this by treating the…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Smart Grid Security and Resilience
