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Found 26 papers, 12 papers with code
Efficiently Adversarial Examples Generation for Visual-Language Models under Targeted Transfer Scenarios using Diffusion Models
Specifically, AdvDiffVLM employs Adaptive Ensemble Gradient Estimation to modify the score during the diffusion model's reverse generation process, ensuring the adversarial examples produced contain natural adversarial semantics and thus possess enhanced transferability.
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory
Vision-language pre-training (VLP) models exhibit remarkable capabilities in comprehending both images and text, yet they remain susceptible to multimodal adversarial examples (AEs).
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds
We find that concealing deformation perturbations in areas insensitive to human eyes can achieve a better trade-off between imperceptibility and adversarial strength, specifically in parts of the object surface that are complex and exhibit drastic curvature changes.
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
However, this paper introduces a novel and previously unrecognized threat in face forgery detection scenarios caused by backdoor attack.
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving.
Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors
The MMP-Attack shows a notable advantage over existing works with superior universality and transferability, which can effectively attack commercial text-to-image (T2I) models such as DALL-E 3.
Does Few-shot Learning Suffer from Backdoor Attacks?
However, in this paper, we propose the Few-shot Learning Backdoor Attack (FLBA) to show that FSL can still be vulnerable to backdoor attacks.
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation
Current Visual-Language Pre-training (VLP) models are vulnerable to adversarial examples.
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization
Investigating the generation of high-transferability adversarial examples is crucial for uncovering VLP models' vulnerabilities in practical scenarios.
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation
At the second stage, the pixels are divided into different branches based on their transferable property which is dependent on Kullback-Leibler divergence.
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
This survey explores the landscape of the adversarial transferability of adversarial examples.
Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks
Besides, we provide theoretical analysis to show the model robustness can be improved by the single-step adversarial training with sampled subnetworks.
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging
In this paper, we conduct a comprehensive study of over 10 fast adversarial training methods in terms of adversarial robustness and training costs.
Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training
Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Fast Adversarial Training with Prior-Guided Knowledge
This initialization is generated by using high-quality adversarial perturbations from the historical training process.
Context-Aware Robust Fine-Tuning
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sentence "a [CONTEXT] of [CLASS]".
Ranked #1 on
Domain Generalization
on VLCS
A Large-scale Multiple-objective Method for Black-box Attack against Object Detection
Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information.
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features.
Prior-Guided Adversarial Initialization for Fast Adversarial Training
Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process.
Adversarial Attack
Adversarial Attack on Video Classification
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.
LAS-AT: Adversarial Training with Learnable Attack Strategy
In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness.
Defending against Model Stealing via Verifying Embedded External Features
In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified \emph{external features}.
Boosting Fast Adversarial Training with Learnable Adversarial Initialization
Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training.
An Effective and Robust Detector for Logo Detection
Moreover, we have applied the proposed methods to competition ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi platform and won top 2 in 36489 teams.
Identifying and Resisting Adversarial Videos Using Temporal Consistency
We propose the temporal defense, which reconstructs the polluted frames with their temporally neighbor clean frames, to deal with the adversarial videos with sparse polluted frames.
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples
In other words, ComDefend can transform the adversarial image to its clean version, which is then fed to the trained classifier.
