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Found 13 papers, 11 papers with code
Practical Deep Dispersed Watermarking with Synchronization and Fusion
Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5. 28\% and 5. 93\% against single and combined attacks, respectively, and show less file size increment and better visual quality.
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
However, our method still outperforms existing methods when attacking transformers.
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks
Unrestricted color attacks, which manipulate semantically meaningful color of an image, have shown their stealthiness and success in fooling both human eyes and deep neural networks.
Frequency Domain Model Augmentation for Adversarial Attack
Specifically, we apply a spectrum transformation to the input and thus perform the model augmentation in the frequency domain.
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack
Specifically, by observing that adversarial examples to a specific defense model follow some regularities in their starting points, we design an Adaptive Direction Initialization strategy to speed up the evaluation.
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation
In this paper, we move a step forward and show the existence of a \textbf{training-free} adversarial perturbation under the no-box threat model, which can be successfully used to attack different DNNs in real-time.
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
Notably, our methods outperform state-of-the-art approaches by up to 7. 71\% (towards coarse-grained domains) and 25. 91\% (towards fine-grained domains) on average.
Fast Gradient Non-sign Methods
Based on the linearity hypothesis, under $\ell_\infty$ constraint, $sign$ operation applied to the gradients is a good choice for generating perturbations.
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
Feature Space Targeted Attacks by Statistic Alignment
However, the current choice of pixel-wise Euclidean Distance to measure the discrepancy is questionable because it unreasonably imposes a spatial-consistency constraint on the source and target features.
Staircase Sign Method for Boosting Adversarial Attacks
Crafting adversarial examples for the transfer-based attack is challenging and remains a research hot spot.
Patch-wise++ Perturbation for Adversarial Targeted Attacks
Specifically, we introduce an amplification factor to the step size in each iteration, and one pixel's overall gradient overflowing the $\epsilon$-constraint is properly assigned to its surrounding regions by a project kernel.
Patch-wise Attack for Fooling Deep Neural Network
By adding human-imperceptible noise to clean images, the resultant adversarial examples can fool other unknown models.
