Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs’ vulnerability to Adversarial Examples (AE) hinders their deployments to safety-critical applications. In this paper, we present BEYOND, an innovative AE detection frameworkdesigned for reliable predictions. BEYOND identifies AEs by distinguishing the AE’s abnormal relation with its augmented versions, i.e. neighbors, from two prospects: representation similarity and label consistency. An off-the-shelf Self-Supervised Learning (SSL) model is used to extract the representation and predict the label for its highly informative representation capacity compared to supervised learning models. We found clean samples maintain a high degree of representation similarity and label consistency relative to their neighbors, in contrast to AEs which exhibit significant discrepancies. We explain this obser vation and show that leveraging this discrepancy BEYOND can accurately detect AEs. Additionally, we develop a rigorous justification for the effectiveness of BEYOND. Furthermore, as a plug-and-play model, BEYOND can easily cooperate with the Adversarial Trained Classifier (ATC), achieving state-of-the-art (SOTA) robustness accuracy. Experimental results show that BEYOND outperforms baselines by a large margin, especially under adaptive attacks. Empowered by the robust relationship built on SSL, we found that BEYOND outperforms baselines in terms of both detection ability and speed.
Figure 1. Neighborhood Relations of AEs and Clean Samples.
Latent Neighborhood Graph (LNG) represents the relationship between the input sample and the reference sample as a graph, whose nodes are embeddings extracted by DDN and edges are built according to distances between the input node and reference nodes, and train a graph neural network to detect AEs.
Figure 2. Overview of BEYOND. First, we augment the input image to obtain a bunch of its neighbors. Then, we perform the label consistency detection mechanism on the classifier’s prediction of the input image and that of neighbors predicted by SSL’s classification head. Meanwhile, the representation similarity mechanism employs cosine distance to measure the similarity among the input image and its neighbors. Finally, The input image with poor label consistency or representation similarity is flagged as AE.
| AUC(%) | Unseen: Attacks used in training are preclude from tests | Seen: Attacks used in training are included in tests | |||||||
|---|---|---|---|---|---|---|---|---|---|
| FGSM | PGD | AutoAttack | Square | FGSM | PGD | CW | AutoAttack | Square | |
| DkNN | 61.55 | 51.22 | 52.12 | 59.46 | 61.55 | 51.22 | 61.52 | 52.12 | 59.46 |
| kNN | 61.83 | 54.52 | 52.67 | 73.39 | 61.83 | 54.52 | 62.23 | 52.67 | 73.39 |
| LID | 71.08 | 61.33 | 55.56 | 66.18 | 73.61 | 67.98 | 55.68 | 56.33 | 85.94 |
| Hu | 84.51 | 58.59 | 53.55 | 95.82 | 84.51 | 58.59 | 91.02 | 53.55 | 95.82 |
| Mao | 95.33 | 82.61 | 81.95 | 85.76 | 95.33 | 82.61 | 83.10 | 81.95 | 85.76 |
| LNG | 98.51 | 63.14 | 58.47 | 94.71 | 99.88 | 91.39 | 89.74 | 84.03 | 98.82 |
| BEYOND | 98.89 | 99.28 | 99.16 | 99.27 | 98.89 | 99.28 | 99.20 | 99.16 | 99.27 |
Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model and the detection strategy. For an SSL model with a feature extractor f, a projector h, and a classification head g, the classification branch can be formulated as C= f ° g and the representation branch as R = f ° h. To attack effectively, the adversary must deceive the target model while guaranteeing the label consistency and representation similarity of the SSL model.
where k represents the number of generated neighbors, yt is the target class, and L is the cross entropy loss function.
@article{he2024beyond,
author = {Zhiyuan, He and Yijun, Yang and Pin-Yu, Chen and Qiang, Xu and Tsung-Yi, Ho},
title = {Be your own neighborhood: Detecting adversarial example by the neighborhood relations built on self-supervised learning},
journal = {ICML},
year = {2024},
}