Deep neural networks have shown their vulnerability to adversarial attacks. In this paper, we focus on sparse adversarial attack based on the $\ell_0$ norm constraint, which can succeed by only modifying a few pixels of an image. Despite a high attack success rate, prior sparse attack methods achieve a low transferability under the black-box protocol due to overfitting the target model. Therefore, we introduce a generator architecture to alleviate the overfitting issue and thus efficiently craft transferable sparse adversarial examples. Specifically, the generator decouples the sparse perturbation into amplitude and position components. We carefully design a random quantization operator to optimize these two components jointly in an end-to-end way. The experiment shows that our method has improved the transferability by a large margin under a similar sparsity setting compared with state-of-the-art methods. Moreover, our method achieves superior inference speed, 700$\times$ faster than other optimization-based methods. The code is available at https://github.com/shaguopohuaizhe/TSAA.
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；RealAI；Tsinghua-Bosch Joint ML Center（清华博世联合机器学习中心）；BNRist Center, Tsinghua University（清华大学BNRist中心）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Adversarial attacks with improved transferability - the ability of an adversarial example crafted on a known model to also fool unknown models - have recently received much attention due to their practicality. Nevertheless, existing transferable attacks craft perturbations in a deterministic manner and often fail to fully explore the loss surface, thus falling into a poor local optimum and suffering from low transferability. To solve this problem, we propose Attentive-Diversity Attack (ADA), which disrupts diverse salient features in a stochastic manner to improve transferability. Primarily, w…
The transferability and robustness of adversarial examples are two practical yet important properties for black-box adversarial attacks. In this paper, we explore effective mechanisms to boost both of them from the perspective of network hierarchy, where a typical network can be hierarchically divided into output stage, intermediate stage and input stage. Since over-specialization of source model, we can hardly improve the transferability and robustness of the adversarial perturbations in the output stage. Therefore, we focus on the intermediate and input stages in this paper and propose a tra…
In recent years, the trackers based on Siamese networks have emerged as highly effective and efficient for visual object tracking (VOT). While these methods were shown to be vulnerable to adversarial attacks, as most deep networks for visual recognition tasks, the existing attacks for VOT trackers all require perturbing the search region of every input frame to be effective, which comes at a non-negligible cost, considering that VOT is a real-time task. In this paper, we propose a framework to generate a single temporally transferable adversarial perturbation from the object template image onl…
Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., black-box attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degrade prediction accuracy in a source model without aware of intrinsic features of objects in the images. We argue that such brute-force degradation would introduce model-specific local optimum into adversarial examples, thus limiting the transferability. By contrast, we propose the Feature Importanc…
Peking University（北京大学）；University of Waterloo（滑铁卢大学）
Transfer-based adversarial example is one of the most important classes of black-box attacks. However, there is a trade-off between transferability and imperceptibility of the adversarial perturbation. Prior work in this direction often requires a fixed but large $\ell_p$-norm perturbation budget to reach a good transfer success rate, leading to perceptible adversarial perturbations. On the other hand, most of the current unrestricted adversarial attacks that aim to generate semantic-preserving perturbations suffer from weaker transferability to the target model. In this work, we propose a geo…
Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training wit…
Xiamen University（厦门大学）；University of Trento（特伦托大学）
Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted adversarial examples with higher transferability than non-targeted ones. Recent studies reveal that the traditional Cross-Entropy (CE) loss function is insufficient to learn transferable targeted adversarial examples due to the issue of vanishing gradient. In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly ob…
The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community. Three approaches, optimizing standard objective functions, exploiting attention maps, and smoothing decision surfaces, are commonly used to craft adversarial examples. By tightly integrating the three approaches, we propose a new and simple algorithm named Transferable Attack based on Integrated Gradients (TAIG) in this paper, which can find highly transferable adversarial examples for black-box attacks. Unlike previous methods using multiple computational terms or combining wit…
CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Pengcheng Laboratory（鹏城实验室）；Shenzhen University（深圳大学）；Shenzhen Digital Certificate Authority CO., Ltd（深圳数字证书认证中心有限公司）
Towards Highly Transferable Vision-Language Attack via Semantic-Augmented Dynamic Contrastive Interaction
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）；Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（Surrey 大学视觉、语音和信号处理中心）
Deep neural networks have shown to be very vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to benign inputs. After achieving impressive attack success rates in the white-box setting, more focus is shifted to black-box attacks. In either case, the common gradient-based approaches generally use the $sign$ function to generate perturbations at the end of the process. However, only a few works pay attention to the limitation of the $sign$ function. Deviation between the original gradient and the generated noises may lead to inaccurate gradient update estimati…
Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial examples, which mislead classifiers by applying human-imperceptible perturbations on clean inputs. Under the query-free black-box scenario, adversarial examples are hard to transfer to unknown models, and several methods have been proposed with the low transferability. To settle such issue, we design a max-min framework inspired by input transformations, which are benificial to both the adversarial attack and defense. Explicitly, we decrease loss values with inputs' affline transformations as…
Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness. Several state-of-the-art defenses are shown to be robust against transferable adversarial examples. In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the de…
University of Siena（锡耶纳大学）；University of Florence（佛罗伦萨大学）
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds. On one hand, these environments could also become a mean to study the weaknesses of Machine Learning algorithms, or to simulate training settings that allow Machine Learning models to gain robustness to 3D adversarial attacks. On the other hand, their growing popularity might also attract those that aim at creating adversarial conditions to invalidate the benchmarking process, especially in t…
SegTrans: Transferable Adversarial Examples for Segmentation Models
School of Cyber Science and Engineering, Huazhong University of Science and Technology（电子科学与工程学院，华中科技大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）；School of Software Engineering, Huazhong University of Science and Technology（软件工程学院，华中科技大学）；School of Information and Communication Technology, Griffith University（信息与通信技术学院，格里菲斯大学）
Transferable Adversarial Attack on Image Tampering Localization
Communication University of China（中国传媒大学）；State Key Laboratory of Media Convergence and Communication, Communication University of China（中国传媒大学媒体融合与传播国家重点实验室）；School of Computer and Cyber Sciences, Communication University of China（中国传媒大学计算机与网络空间安全学院）
PLA Strategy Support Force Information Engineering University（中国人民解放军战略支援部队信息工程大学）；University of Science and Technology of China（中国科学技术大学）
Adversarial attacks are considered the intrinsic vulnerability of CNNs. Defense strategies designed for attacks have been stuck in the adversarial attack-defense arms race, reflecting the imbalance between attack and defense. Dynamic Defense Framework (DDF) recently changed the passive safety status quo based on the stochastic ensemble model. The diversity of subnetworks, an essential concern in the DDF, can be effectively evaluated by the adversarial transferability between different networks. Inspired by the poor adversarial transferability between subnetworks of scratch tickets with various…
Deep Neural Networks have been found vulnerable re-cently. A kind of well-designed inputs, which called adver-sarial examples, can lead the networks to make incorrectpredictions. Depending on the different scenarios, goalsand capabilities, the difficulties of the attacks are different.For example, a targeted attack is more difficult than a non-targeted attack, a universal attack is more difficult than anon-universal attack, a transferable attack is more difficultthan a nontransferable one. The question is: Is there existan attack that can meet all these requirements? In this pa-per, we answer …
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Oxford（牛津大学）
Recent development of adversarial attacks has proven that ensemble-based methods outperform traditional, non-ensemble ones in black-box attack. However, as it is computationally prohibitive to acquire a family of diverse models, these methods achieve inferior performance constrained by the limited number of models to be ensembled. In this paper, we propose Ghost Networks to improve the transferability of adversarial examples. The critical principle of ghost networks is to apply feature-level perturbations to an existing model to potentially create a huge set of diverse models. After that, mode…
Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations are easily overfitting with a single surrogate model and specific data pattern. In this paper, we propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. For data …
Visual-Invariance-Augmented Feature Optimal Alignment for Transferable Adversarial Attacks against Closed-Source MLLMs
Nanyang Technological University（南洋理工大学）；Northeastern University（东北大学）；Chongqing University（重庆大学）；University of Chinese Academy of Sciences（中国科学院大学）；Sun Yat-sen University（中山大学）
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University（复旦大学计算机科学技术学院上海市智能信息处理重点实验室）；Shanghai Collaborative Innovation Center on Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Department of Computer Science, University of Maryland（马里兰大学计算机科学系）
Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. % crafted in a similar fashion as CNNs. In this paper, we posit that adversarial attacks on transformers should be specially tailored for their architecture, jointly considering both patches and self-attention, in order to achieve high transferability. More specifically, we introduce a dual attack framework, which contains a Pay No Attention (PNA) attack and a PatchOut attack, to improve the transferability of adversarial samples across diff…
Recent studies have shown that adversarial examples hand-crafted on one white-box model can be used to attack other black-box models. Such cross-model transferability makes it feasible to perform black-box attacks, which has raised security concerns for real-world DNNs applications. Nevertheless, existing works mostly focus on investigating the adversarial transferability across different deep models that share the same modality of input data. The cross-modal transferability of adversarial perturbation has never been explored. This paper investigates the transferability of adversarial perturba…
SAM Encoder Breach by Adversarial Simplicial Complex Triggers Downstream Model Failures
State Key Laboratory of Advanced Rail Autonomous Operation（先进轨道交通自主运行状态关键实验室）；Beijing Key Lab of Traffic Data Mining and Embodied Intelligence（北京交通数据挖掘与具身智能重点实验室）；Collaborative Innovation Center of Railway Traffic Safety（铁路交通安全协同创新中心）；National Engineering Research Center of Rail Transportation Operation and Control System（铁路运输操作与控制系统国家工程研究中心）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；School of Automation and Intelligence, Beijing Jiaotong University（北京交通大学自动化与智能学院）
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Collaborative Innovation Center of Railway Traffic Safety（轨道交通安全协同创新中心）
School of Software, BNRist, Tsinghua University（清华大学软件学院、北京信息科学与技术国家研究中心（BNRist））；Dept. of Comp. Sci. and Tech., BNRist Center, Institute for AI, THBI Lab, Tsinghua University（清华大学计算机科学与技术系、北京信息科学与技术国家研究中心（BNRist）中心、人工智能研究院、THBI实验室）
We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance in the score-based setting. SWITCH features a highly efficient and effective utilization of the gradient of a surrogate model $\hat{\mathbf{g}}$ w.r.t. the input image, i.e., the transferable gradient. In each iteration, SWITCH first tries to update the current sample along the direction of $\hat{\mathbf{g}}$, but considers switching to its opposite direction $-\hat{\mathbf{g}}$ if our algorithm detects that it does not increase the value of the attack objective fu…
One Attack to Fool Them All: Highly Transferable Black-Box Adversarial Attacks on Frontier MLLMs
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Transferable Dual-Domain Feature Importance Attack against AI-Generated Image Detector
School of Computer and Cyber Sciences, Communication University of China（通信大学计算机与电子科学学院）；State Key Laboratory of Media Convergence and Communication, Communication University of China（通信大学媒体融合与传播国家重点实验室）；School of Computer Science and Engineering, Hunan University of Information Technology（湖南信息科技学院计算机科学与工程学院）；Department of Information Engineering, Beijing Institute of Graphic Communication（北京印刷学院信息工程系）；Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology（福建科技学院大数据挖掘与应用省级重点实验室）
Sparse and Transferable Universal Singular Vectors Attack
Sber AI Lab（Sber AI实验室）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））
Army Military Transportation University（陆军军事交通学院）；Army Engineering University（陆军工程大学）
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 …
Visually realistic GAN-generated images have recently emerged as an important misinformation threat. Research has shown that these synthetic images contain forensic traces that are readily identifiable by forensic detectors. Unfortunately, these detectors are built upon neural networks, which are vulnerable to recently developed adversarial attacks. In this paper, we propose a new anti-forensic attack capable of fooling GAN-generated image detectors. Our attack uses an adversarially trained generator to synthesize traces that these detectors associate with real images. Furthermore, we propose …
We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: $l_0$-bounded perturbations, adversarial patches, and adversarial frames. The $l_0$-version of untargeted Sparse-RS outperforms all black-box and even all white-box attacks for different models on MNIST, CIFAR-10, and ImageNet. Moreover, our untargeted Sparse-RS achieves very high success rates even for th…
Chengdu University of Information Technology（成都信息工程大学）；Keya Medical（科亚医疗）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Microsoft Research Asia（微软亚洲研究院）
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a surge of research in this direction, including adversarial attacks on object detection networks. However, previous studies are dedicated to attacking anchor-based object detectors. In this paper, we present the first adversarial attack on anchor-free object detectors. It conducts category-wise, instead of previously instance-wise, attacks on object detectors, and leverages high-level semantic information to efficiently …
Beijing University of Posts and Telecommunications（北京邮电大学）
Face recognition has achieved great success in the last five years due to the development of deep learning methods. However, deep convolutional neural networks (DCNNs) have been found to be vulnerable to adversarial examples. In particular, the existence of transferable adversarial examples can severely hinder the robustness of DCNNs since this type of attacks can be applied in a fully black-box manner without queries on the target system. In this work, we first investigate the characteristics of transferable adversarial attacks in face recognition by showing the superiority of feature-level m…
Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective
Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学网络安全研究院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；Department of Automation, University of Science and Technology of China（中国科学技术大学自动化系）；Institute for Math & AI, Wuhan University（武汉大学数学与人工智能研究院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
Transferable adversarial attacks optimize adversaries from a pretrained surrogate model and known label space to fool the unknown black-box models. Therefore, these attacks are restricted by the availability of an effective surrogate model. In this work, we relax this assumption and propose Adversarial Pixel Restoration as a self-supervised alternative to train an effective surrogate model from scratch under the condition of no labels and few data samples. Our training approach is based on a min-max scheme which reduces overfitting via an adversarial objective and thus optimizes for a more gen…
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
The University of Sydney（悉尼大学）；University of Malaya（马来亚大学）；Suzhou Yierqi（苏州意尔奇）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Wollongong（伍伦贡大学）
