Data-efficient backdoor attacks
WebDataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. ... This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the ... WebJun 29, 2024 · The function \(\Theta\) transforms clean data in any label into backdoor data that have the trigger \(\phi\). Since CBA and DBA are two classic and efficient backdoor attacks in FL, we adopt them as benchmarks to evaluate our …
Data-efficient backdoor attacks
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WebFeb 10, 2024 · Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. WebData-efficient Large Scale Place Recognition with Graded Similarity Supervision Maria Leyva-Vallina · Nicola Strisciuglio · Nicolai Petkov ... Progressive Backdoor Erasing via …
WebFeb 13, 2024 · More precisely, backdoor triggers in neuromorphic data can change their position and color, allowing a larger range of possibilities than common triggers in, e.g., the image domain. We propose different attacks achieving up to 100\% attack success rate without noticeable clean accuracy degradation. WebMar 1, 2024 · One of the most efficient attacks to ANNs which are considered as a serious threat to security–critical systems are called backdoor (BD) (Gu, Dolan-Gavitt, & Garg, 2024) or Trojan attacks (Liu et al., 2024).
WebThe experimental results on CIFAR-10 and ImageNet-10 indicate that the proposed method is effective: the same attack success rate can be achieved with only 47% to 75% of the … WebData-Efficient Backdoor Attacks Pengfei Xia, Ziqiang Li, Wei Zhang and Bin Li University of Science and Technology of China, Hefei, China …
WebApr 22, 2024 · The experimental results on CIFAR-10 and ImageNet-10 indicate that the proposed method is effective: the same attack success rate can be achieved with only …
WebJul 1, 2024 · The data-efficient backdoor attack [203] controlled the choice of which samples to poison according to a filtering-and-updating strategy, which showed improved attack performance compared... razook\u0027s drug stillwater okWebSep 12, 2024 · Current backdoor attacks rely on generating triggers in the image/pixel domain; however, as we show in this paper, it is not the only domain to exploit and one should always "check the other doors". In this work, we propose a complete pipeline for generating a dynamic, efficient, and invisible backdoor attack in the frequency domain. d\\u0027angelo\\u0027s menuWebDataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset … d\u0027angelo\u0027s pizza \u0026 pastaWebFeb 19, 2024 · We propose an efficient target/victim pairs identification approach for backdoor detection based on static weight analysis. It is agnostic to model structures and trigger types and thus can significantly improve the efficiency of backdoor detection for local patch attacks and global transformation attacks. razook\\u0027s drug stillwater okWebJan 7, 2024 · Deep neural network (DNNs) provide excellent performance in image recognition, speech recognition, video recognition, and pattern analysis. However, DNNs are vulnerable to backdoor attacks. A backdoor attack allows a DNN to correctly recognize normal data that do not contain a specific trigger but induces it to incorrectly recognize … d\u0027angelo\u0027s menu riWebOct 12, 2024 · In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired target label when a predefined trigger is added to test inputs. d\u0027angelo\u0027s brockton maWebA Comprehensive Benchmark for Evaluating Backdoor Attacks and Defenses Get Started Backdoor learning is an emerging topic of studying the adversarial vulnerability of machine learning models during the training stage. Many backdoor attack and defense methods have been developed in recent ML and Security conferences/journals. razooma net photography