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Boosting the Transferability of Adversarial Samples via Attention

https://doi.org/10.1109/cvpr42600.2020.00124

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01258-8_10openalex-snapshot

Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms

inferredcites →https://doi.org/10.1007/978-3-030-01264-9_28openalex-snapshot

Transferable Adversarial Perturbations

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_38openalex-snapshot

Identity Mappings in Deep Residual Networks

inferredcites →https://doi.org/10.1007/978-3-319-94042-7_11openalex-snapshot

Adversarial Attacks and Defences Competition

inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot

ImageNet Large Scale Visual Recognition Challenge

inferredcites →https://doi.org/10.1016/j.patcog.2018.07.023openalex-snapshot

Wild patterns: Ten years after the rise of adversarial machine learning

inferredcites →https://doi.org/10.1016/j.tics.2007.09.004openalex-snapshot

Learning multiple layers of representation

inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot

Rethinking the Inception Architecture for Computer Vision

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot

Universal Adversarial Perturbations

inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot

Boosting Adversarial Attacks with Momentum

inferredcites →https://doi.org/10.1109/cvpr.2019.00284openalex-snapshot

Improving Transferability of Adversarial Examples With Input Diversity

inferredcites →https://doi.org/10.1109/cvpr.2019.00444openalex-snapshot

Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

inferredcites →https://doi.org/10.1109/dsn.2019.00026openalex-snapshot

Deep Validation: Toward Detecting Real-World Corner Cases for Deep Neural Networks

inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot

The Limitations of Deep Learning in Adversarial Settings

inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot

Towards Evaluating the Robustness of Neural Networks

inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot

Practical Black-Box Attacks against Machine Learning

inferredcites →https://doi.org/10.1145/3234150openalex-snapshot

A Survey on Deep Learning

inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot

Adversarial Examples in the Physical World

inferredcites →https://doi.org/10.14722/ndss.2018.23198openalex-snapshot

Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

inferredcites →https://doi.org/10.1609/aaai.v32i1.11302openalex-snapshot

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot

On Assessing ML Model Robustness: A Methodological Framework (Academic Track)

inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot

Intriguing properties of neural networks

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot

Explaining and Harnessing Adversarial Examples

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot

Delving into Transferable Adversarial Examples and Black-box Attacks

inferredcites →https://doi.org/10.48550/arxiv.1705.07204openalex-snapshot

Ensemble Adversarial Training: Attacks and Defenses

inferredcites →https://doi.org/10.48550/arxiv.1707.04131openalex-snapshot

Foolbox: A Python toolbox to benchmark the robustness of machine learning models

inferredcites →https://doi.org/10.48550/arxiv.1810.01268openalex-snapshot

CAAD 2018: Generating Transferable Adversarial Examples

inferredcites →https://doi.org/10.48550/arxiv.1902.06705openalex-snapshot

On Evaluating Adversarial Robustness

inferredcites →https://doi.org/10.48550/arxiv.1905.07121openalex-snapshot

Simple Black-box Adversarial Attacks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Alternating Minimization Adversarial Patch

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Attention‐guided transformation‐invariant attack for black‐box adversarial examples

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Adversarial Attacks and Defenses in Deep Learning: A Survey

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Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors

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Improving Adversarial Transferability via Model Alignment

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A Whale Falls, All Thrive: Mitigating Attention Gap to Improve Adversarial Transferability

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DiSCo: Disrupting Semantic Consistency for Transferable Cross-Modal Adversarial Attacks

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Improving Transferability of Adversarial Examples by SVD Transformation

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Enabling Heterogeneous Adversarial Transferability via Feature Permutation Attacks

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ADPF: Adversarial Sample Detection Based on Prediction Feature

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PFA: Improving Adversarial Transferability via Prediction Feature

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Enhancing adversarial transferability with partial blocks on vision transformer

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Improving transferable adversarial attack for vision transformers via global attention and local drop

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Channel-augmented joint transformation for transferable adversarial attacks

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Improving the transferability of adversarial attacks via self-ensemble

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Improving the transferability of adversarial examples with path tuning

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Improving the transferability of adversarial examples via the high-level interpretable features for object detection

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Generate Transferable Adversarial Physical Camouflages via Triplet Attention Suppression

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Learning Generalizable Mixed-Precision Quantization via Attribution Imitation

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What Do Visual Models Look At? Dilated Attention for Targeted Transferable Attacks

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SEIDA: stable and effective image degradation attack

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Dual stage black-box adversarial attack against vision transformer

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DIB-UAP: enhancing the transferability of universal adversarial perturbation via deep information bottleneck

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JDA-attack: leveraging joint multimodal data augmentation to enhance adversarial transferability of vision-language pre-training models

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Enhancing the Transferability of Adversarial Patch via Alternating Minimization

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Generating De-identification facial images based on the attention models and adversarial examples

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On-manifold adversarial attack based on latent space substitute model

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A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions

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CFOA: Exploring transferable adversarial examples by content feature optimization with attention guidance

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TransRPN: Towards the Transferable Adversarial Perturbations using Region Proposal Networks and Beyond

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Adaptive adversarial pattern contrast algorithm for black-box model and domain attack

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DI-AA: An interpretable white-box attack for fooling deep neural networks

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AdvDPDiff: A diffusion model adversarial attacks strategy combining multi-model ensemble and dynamic input transformation

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BSR-SAGE: Sharpness-aware gradient ensemble for transferable adversarial attacks

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Exploring transferable and robust adversarial perturbation generation across network hierarchy

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Robustness evaluation of image classification models based on edge features: Tight and Non-Tight boundaries

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Boosting the transferability of adversarial examples via stochastic serial attack

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Strengthening transferability of adversarial examples by adaptive inertia and amplitude spectrum dropout

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Adversarial Example Generation Method Based on Sensitive Features

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A Survey of Adversarial Examples in Computer Vision: Attack, Defense, and Beyond

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Boosting the Transferability of Adversarial Examples with More Efficient Data Augmentation

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An Adversarial Example Generation Method Based on Mask Extraction

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Residual Vector Capsule: Improving Capsule by Pose Attention

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Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey

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Intermediate-Layer Transferable Adversarial Attack With DNN Attention

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Enhancing the Transferability of Adversarial Attacks for End-to-End Autonomous Driving Systems

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A Transferable Adversarial Attack Algorithm Based On Spatial Transformation

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Generating Transferable Perturbations via Intermediate Feature Attention Weighting

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Transferable Adversarial Attacks on Transformer and CNN

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CFA: Consistent Feature Attribution for Black-Box Adversarial Attack Against Object Detection

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Efficient Transferability of Generative Perturbations with Salient Feature Disruption and Adversarial Transformation

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EvoIBA: Evolutionary Boundary Attack Under Hard-Label Black Box Condition

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ASL: Adversarial Attack by Stacking Layer-wise Relevance Propagation

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TDBA: Towards Transferable Data-free Black-box Attack with Stable Diffusion

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Exploring Adversarial Fake Images on Face Manifold

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Improving the Transferability of Adversarial Samples with Adversarial Transformations

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Improving Adversarial Transferability via Neuron Attribution-based Attacks

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Cross-Modal Transferable Adversarial Attacks from Images to Videos

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Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input

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Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

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Improving the Transferability of Adversarial Samples by Path-Augmented Method

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Enhancing the Self-Universality for Transferable Targeted Attacks

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Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization

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Unsegment Anything by Simulating Deformation

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Boosting Adversarial Transferability by Block Shuffle and Rotation

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Improving Transferable Targeted Adversarial Attacks with Model Self-Enhancement

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SkipPLUS: Skip the First Few Layers to Better Explain Vision Transformers

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Critical Features are Achilles’ Heels: Efficient Personalized Text-to-Image Model Attack through Delicate Feature Perturbation

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Stochastic Perturbation Averaging Boosts Transferability of Adversarial Examples

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Scale-Exchange Attack: Enhancing Adversarial Transferability via Multi-scale Block Transformation and Replacement

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Multi-Layer Feature Division Transferable Adversarial Attack

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Enhance Transferability of Adversarial Examples with Model Architecture

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Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation

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On Generating Transferable Targeted Perturbations

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LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions

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Structure Invariant Transformation for better Adversarial Transferability

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Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods

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Research on Black-Box Adversarial Attack Based on Transformer

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Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks

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GNP Attack: Transferable Adversarial Examples Via Gradient Norm Penalty

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Transferability and Universal Perturbations in Adversarial Attacks: A Review

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Adversarial Attacks and Defenses in Image Classification: A Practical Perspective

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Universally Strict Black-Box Attacks Against Deep Neural Networks

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Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting

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Practical and Efficient Model Extraction of Sentiment Analysis APIs

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Iteration, Mother of Transferability: A Study of Black-Box Iterative Adversarial Attacks Across CNNs and Vision Transformers

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Strength in Simplicity: Evaluating Adversarial Transferability in Quantized Lightweight Convolutional Neural Networks

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Exploiting temporal information to prevent the transferability of adversarial examples against deep fake detectors

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Fighting Attacks on Large Character Set CAPTCHAs Using Transferable Adversarial Examples

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Boosting the Transferability of Adversarial Examples via Adaptive Attention and Gradient Purification Methods

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Detecting Conventional and Adversarial Attacks Using Deep Learning Techniques: A Systematic Review

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Toward Evaluating the Reliability of Deep-Neural-Network-Based IoT Devices

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MAS-PD: Transferable Adversarial Attack Against Vision-Transformers-Based SAR Image Classification Task

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Impact Investigation of Adversarial Samples on CNN-Based SAR Image Target Detectors

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SoK: Pitfalls in Evaluating Black-Box Attacks

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Run Away From the Original Example and Towards Transferability

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A Local Perturbation Generation Method for GAN-Generated Face Anti-Forensics

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Exploring Cross-Domain Transferable Attacks via Decoupling Features

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Enhancing the Security of Large Character Set CAPTCHAs Using Transferable Adversarial Examples

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Latent Danger Zone: Distilling Unified Attention for Cross-Architecture Black-Box Attacks

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Generalization-Enhanced Feature-Wise and Correlation Attentions for Cross-Database Facial Expression Recognition

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Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness

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Pluggable Attack for Visual Object Tracking

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A Unified Optimization Framework for Feature-Based Transferable Attacks

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Improving Transferability of Adversarial Samples via Critical Region-Oriented Feature-Level Attack

inferred← citeshttps://doi.org/10.1109/tifs.2024.3465212openalex-snapshot

Boosting Adversarial Transferability via Logits Mixup With Dominant Decomposed Feature

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Enhancing the Transferability of Adversarial Attacks via Multi-Feature Attention

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Boosting Adversarial Transferability via Relative Feature Importance-Aware Attacks

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Exploiting Shared Adversarial Features for Dynamic Attacks in Large Vision-Language Models

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CAA: Toward Camouflaged and Transferable Adversarial Examples

inferred← citeshttps://doi.org/10.1109/tip.2023.3331582openalex-snapshot

Towards Transferable Adversarial Attacks on Image and Video Transformers

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A Highly Transferable Camouflage Attack Against Object Detectors in the Physical World

inferred← citeshttps://doi.org/10.1109/tkde.2026.3709786openalex-snapshot

Data-Centric Challenges, Techniques, and Impacts: A Survey on Image Data Perturbation

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AutoMA: Towards Automatic Model Augmentation for Transferable Adversarial Attacks

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Boosting Adversarial Transferability With Learnable Patch-Wise Masks

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Enhancing Cross-Task Transferability of Adversarial Examples via Spatial and Channel Attention

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C-FA: Enhancing Transferability Through Contrastive Learning and Feature Attention in Point Cloud Attacks

inferred← citeshttps://doi.org/10.1109/tpami.2023.3347835openalex-snapshot

Adaptive Cross-Modal Transferable Adversarial Attacks From Images to Videos

inferred← citeshttps://doi.org/10.1109/tpami.2026.3664421openalex-snapshot

Enhancing Adversarial Transferability With Cost-Efficient Landscape Flattening

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GCMA: Generative Cross-Modal Transferable Adversarial Attacks from Images to Videos

inferred← citeshttps://doi.org/10.1145/3583780.3615095openalex-snapshot

Transferable Structure-based Adversarial Attack of Heterogeneous Graph Neural Network

inferred← citeshttps://doi.org/10.1609/aaai.v36i3.20168openalex-snapshot

Boosting the Transferability of Video Adversarial Examples via Temporal Translation

inferred← citeshttps://doi.org/10.1609/aaai.v36i3.20169openalex-snapshot

Towards Transferable Adversarial Attacks on Vision Transformers

inferred← citeshttps://doi.org/10.1609/aaai.v37i2.25239openalex-snapshot

CDTA: A Cross-Domain Transfer-Based Attack with Contrastive Learning

inferred← citeshttps://doi.org/10.1609/aaai.v37i2.25362openalex-snapshot

Global-Local Characteristic Excited Cross-Modal Attacks from Images to Videos

inferred← citeshttps://doi.org/10.1609/aaai.v37i8.26139openalex-snapshot

Training Meta-Surrogate Model for Transferable Adversarial Attack

inferred← citeshttps://doi.org/10.1609/aaai.v38i3.27947openalex-snapshot

Attacking Transformers with Feature Diversity Adversarial Perturbation

inferred← citeshttps://doi.org/10.1609/aaai.v38i6.28365openalex-snapshot

AGS: Affordable and Generalizable Substitute Training for Transferable Adversarial Attack

inferred← citeshttps://doi.org/10.1609/aaai.v38i6.28430openalex-snapshot

LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate

inferred← citeshttps://doi.org/10.1609/aaai.v38i7.28541openalex-snapshot

Improving the Adversarial Transferability of Vision Transformers with Virtual Dense Connection

inferred← citeshttps://doi.org/10.1609/aaai.v38i7.28542openalex-snapshot

Curvature-Invariant Adversarial Attacks for 3D Point Clouds

inferred← citeshttps://doi.org/10.18653/v1/2021.emnlp-main.121openalex-snapshot

On the Transferability of Adversarial Attacks against Neural Text Classifier

inferred← citeshttps://doi.org/10.21203/rs.3.rs-8787976/v1openalex-snapshot

Boosting Adversarial Transferability for Hyperspectral Image Classification Using Block Transformation and Weighted Intermediate Feature Divergence

inferred← citeshttps://doi.org/10.23919/ccc58697.2023.10240834openalex-snapshot

Adversarial Example Generation Method Based on Probability Histogram Equalization

inferred← citeshttps://doi.org/10.3390/drones7120697openalex-snapshot

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

inferred← citeshttps://doi.org/10.3390/e28060680openalex-snapshot

AMGAA: Attention-Guided Multi-Target Generative Adversarial Attack for Vision Transformers

inferred← citeshttps://doi.org/10.3390/jimaging11110404openalex-snapshot

LatAtk: A Medical Image Attack Method Focused on Lesion Areas with High Transferability

inferred← citeshttps://doi.org/10.3390/math11133015openalex-snapshot

Probability-Distribution-Guided Adversarial Sample Attacks for Boosting Transferability and Interpretability

inferred← citeshttps://doi.org/10.3390/math14132392openalex-snapshot

Boosting the Transferability of Adversarial Attacks with Semantic-Invariance and Low-Gradient Replacement

inferred← citeshttps://doi.org/10.3390/rs16142539openalex-snapshot

Enhancing Transferability with Intra-Class Transformations and Inter-Class Nonlinear Fusion on SAR Images

inferred← citeshttps://doi.org/10.3390/rs17111943openalex-snapshot

Analyzing the Adversarial Robustness and Interpretability of Deep SAR Classification Models: A Comprehensive Examination of Their Reliability

inferred← citeshttps://doi.org/10.36227/techrxiv.175037432.26316641/v1openalex-snapshot

A Survey on Image Data Perturbations: Challenges, Techniques, and Impacts

inferred← citeshttps://doi.org/10.48550/arxiv.2011.08558openalex-snapshot

On the Transferability of Adversarial Attacksagainst Neural Text Classifier

inferred← citeshttps://doi.org/10.48550/arxiv.2012.00909openalex-snapshot

Visually Imperceptible Adversarial Patch Attacks on Digital Images

inferred← citeshttps://doi.org/10.48550/arxiv.2012.11207openalex-snapshot

On Success and Simplicity: A Second Look at Transferable Targeted Attacks

inferred← citeshttps://doi.org/10.48550/arxiv.2105.00433openalex-snapshot

Who's Afraid of Adversarial Transferability?

inferred← citeshttps://doi.org/10.48550/arxiv.2108.07033openalex-snapshot

Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy

inferred← citeshttps://doi.org/10.48550/arxiv.2110.07718openalex-snapshot

Adversarial Attack across Datasets

inferred← citeshttps://doi.org/10.48550/arxiv.2110.10444openalex-snapshot

Moiré Attack (MA): A New Potential Risk of Screen Photos

inferred← citeshttps://doi.org/10.48550/arxiv.2110.12734openalex-snapshot

Fast Gradient Non-sign Methods

inferred← citeshttps://doi.org/10.48550/arxiv.2111.15160openalex-snapshot

Mitigating Adversarial Attacks by Distributing Different Copies to Different Users

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