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Found 12 papers, 9 papers with code
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Over the past decade, a large number of white-box adversarial robustness evaluation methods (i. e., attacks) have been proposed, ranging from single-step to multi-step methods and from individual to ensemble methods.
Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models
Specifically, EBYD first exposes the backdoor functionality in the backdoored model through a model preprocessing step called backdoor exposure, and then applies detection and removal methods to the exposed model to identify and eliminate the backdoor features.
BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on Large Language Models
Generative Large Language Models (LLMs) have made significant strides across various tasks, but they remain vulnerable to backdoor attacks, where specific triggers in the prompt cause the LLM to generate adversary-desired responses.
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
With the advancement of vision transformers (ViTs) and self-supervised learning (SSL) techniques, pre-trained large ViTs have become the new foundation models for computer vision applications.
Shortcuts Everywhere and Nowhere: Exploring Multi-Trigger Backdoor Attacks
Backdoor attacks have become a significant threat to the pre-training and deployment of deep neural networks (DNNs).
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
Distilling Cognitive Backdoor Patterns within an Image
We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks.
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness.
Unlearnable Examples: Making Personal Data Unexploitable
This paper raises the question: \emph{can data be made unlearnable for deep learning models?}
Neural Architecture Search via Combinatorial Multi-Armed Bandit
NAS can be performed via policy gradient, evolutionary algorithms, differentiable architecture search or tree-search methods.
Normalized Loss Functions for Deep Learning with Noisy Labels
However, in practice, simply being robust is not sufficient for a loss function to train accurate DNNs.
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Image Classification
on mini WebVision 1.0
(ImageNet Top-1 Accuracy metric)
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research.
