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- W4310698754 abstract "Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, it still leaves a long way to go for generating transferable adversarial examples under the black-box setting. To this end, this paper proposes to improve the transferability of adversarial examples, and applies dual-stage feature-level perturbations to an existing model to implicitly create a set of diverse models. Then these models are fused by the longitudinal ensemble during the iterations. The proposed method is termed Dual-Stage Network Erosion (DSNE). We conduct comprehensive experiments both on non-residual and residual networks, and obtain more transferable adversarial examples with the computational cost similar to the state-of-the-art method. In particular, for the residual networks, the transferability of the adversarial examples can be significantly improved by biasing the residual block information to the skip connections. Our work provides new insights into the architectural vulnerability of neural networks and presents new challenges to the robustness of neural networks." @default.
- W4310698754 created "2022-12-16" @default.
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- W4310698754 date "2021-12-31" @default.
- W4310698754 modified "2023-10-03" @default.
- W4310698754 title "Adversarial Attack via Dual-Stage Network Erosion" @default.
- W4310698754 doi "https://doi.org/10.48550/arxiv.2201.00097" @default.
- W4310698754 hasPublicationYear "2021" @default.
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