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Toward Adversarial Robustness Network Intrusion Detection Based on Multi-Model Ensemble Approach. [PDF]
Le TT, Cho J, Shin D, Kim H.
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CCIW: Cover-Concealed Image Watermarking for Dual Protection of Privacy and Copyright. [PDF]
Li R, Wang S, Li M, Ren H.
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Individualized Treatment Effect Inference of Head and Neck Cancer with Multimodal Data. [PDF]
Wei Y +5 more
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AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems. [PDF]
Radanliev P.
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Transfer learning for T-cell response prediction. [PDF]
Stadelmaier J, Malone B, Eggeling R.
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Large reasoning models are autonomous jailbreak agents. [PDF]
Hagendorff T, Derner E, Oliver N.
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Learning Universal Adversarial Perturbation by Adversarial Example
Proceedings of the AAAI Conference on Artificial Intelligence, 2022Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li +4 more
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On The Generation of Unrestricted Adversarial Examples
2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2020Adversarial examples are inputs designed by an adversary with the goal of fooling the machine learning models. Most of the research about adversarial examples have focused on perturbing the natural inputs with the assumption that the true label remains unchanged.
Mehrgan Khoshpasand +1 more
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