A Survey on Universal Adversarial Attack.doi:10.24963/IJCAI.2021/635Chaoning ZhangPhilipp BenzChenguo LinAdil KarjauvJing WuIn So KweonInternational Joint Conferences on Artificial Intelligence OrganizationInternational Joint Conference on Artificial Intelligence...
Universal Adversarial Perturbations (UAPs)Moosavi-Dezfooli等人发现一些扰动是图像不可知的(普遍的),并且有很高的概率导致误分类,通过搜索从图像分布中提取的几乎所有数据中导致错误分类的扰动向量,这些扰动也可以很好地推广到多个神经网络。 Universal Adversarial Networks (UAN)将生成网络的输出添加到图像中时,它能够...
8) UNIVERSAL ADVERSARIAL PERTURBATIONS[1610.08401] Universal adversarial perturbations (arxiv.org) 对于FGSM,ILCM,Deep-Fool这些方法,都是对一个图片产生特定的扰动从而来欺骗这个网络,而Moosavi-Dezfooli等人 计算出的'universal' adversarial perturbations(通用对抗扰动)可以对任意图片使用来欺骗这个网络。这些扰动对于...
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning. [link] Zhiyi Tian, Lei Cui, Jie Liang, and Shui Yu. ACM Computing Surveys, 2022. Backdoor Attacks and Defenses in Federated Learning: State-of...
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Survey [pdf] AdvPulse: Universal, Synchronization-free, and Targeted Audio Adversarial Attacks via Subsecond Perturbations. ACM CCS 2020. Attack [pdf] Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information. ACM CCS 2021. Black-box Attack. Physical World [pdf] ...
This survey gives exploratory research on the AI and IoT security system. It also provides a comparative study of a recent IoT oriented datasets; The second question that can be asked is: “can we use the chosen technique to classify and/or detect intrusions and attacks in the IoT security ...
A survey on adversarial attacks and defenses for object detection and their applications in autonomous vehicles While the field of adversarial robustness has witnessed a great deal of achievement in building sophisticated methods of attack and defense, the majority of... A Amirkhani,MP Karimi,A Bani...
input-agnostic方法:比如universal adversarial trigger、natural attack trigger 第二条研究路线:使用额外的模型去捕捉bias 3. 提升模型的鲁棒性 这部分的方法分为四种:data-driven, model and training-scheme based, inductive-prior based 和 causal intervention. 3.1 数据驱动方法 使用定制化的数据增强方法构造数据(i...
23.11Harbin Institute of TechnologyarxivA Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions 23.11Arizona State UniversityarxivCan Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey DateTypeTitleURL ...