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Found 12 papers, 4 papers with code
Object-Centric Diffusion for Efficient Video Editing
Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts.
Investigating Top-$k$ White-Box and Transferable Black-box Attack
It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength.
Investigating Top-k White-Box and Transferable Black-Box Attack
It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength.
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Thus, it is critical for the community to know whether the newly proposed ViT and MLP-Mixer are also vulnerable to adversarial attacks.
Universal Adversarial Training with Class-Wise Perturbations
The SOTA universal adversarial training (UAT) method optimizes a single perturbation for all training samples in the mini-batch.
A Survey On Universal Adversarial Attack
The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i. e. a single perturbation to fool the target DNN for most images.
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
We perform task-specific and joint analysis and reveal that (a) frequency is a key factor that influences their performance based on the proposed entropy metric for quantifying the frequency distribution; (b) their success can be attributed to a DNN being highly sensitive to high-frequency content.
Data-Free Universal Adversarial Perturbation and Black-Box Attack
For a more practical universal attack, our investigation of untargeted UAP focuses on alleviating the dependence on the original training samples, from removing the need for sample labels to limiting the sample size.
Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach
Recently, deep learning has shown large success in data hiding, while non-differentiability of JPEG makes it challenging to train a deep pipeline for improving robustness against lossy compression.
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
This is the first work demonstrating the success of (DNN-based) hiding a full image for watermarking and LFM.
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
Adversarial training is the most widely used technique for improving adversarial robustness to strong white-box attacks.
Revisiting Batch Normalization for Improving Corruption Robustness
We find that simply estimating and adapting the BN statistics on a few (32 for instance) representation samples, without retraining the model, improves the corruption robustness by a large margin on several benchmark datasets with a wide range of model architectures.
