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Found 21 papers, 10 papers with code
PAL: Proxy-Guided Black-Box Attack on Large Language Models
In this work, we introduce the Proxy-Guided Attack on LLMs (PAL), the first optimization-based attack on LLMs in a black-box query-only setting.
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
However, a direct application of LipSDP to the resultant residual ReLU networks is conservative and even fails in recovering the well-known fact that the MaxMin activation is 1-Lipschitz.
Towards Real-World Focus Stacking with Deep Learning
Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shallow depth of field and different focus planes.
LipSim: A Provably Robust Perceptual Similarity Metric
On the other hand, as perceptual metrics rely on neural networks, there is a growing concern regarding their resilience, given the established vulnerability of neural networks to adversarial attacks.
Certification of Deep Learning Models for Medical Image Segmentation
In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models.
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
The certified radius in this context is a crucial indicator of the robustness of models.
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
In this paper, we propose the Robust Learned Perceptual Image Patch Similarity (R-LPIPS) metric, a new metric that leverages adversarially trained deep features.
Towards Better Certified Segmentation via Diffusion Models
The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy.
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models.
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge.
A Unified Algebraic Perspective on Lipschitz Neural Networks
Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant.
Ranked #1 on
Provable Adversarial Defense
on CIFAR-100
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
We first show that these certificates use too little information about the classifier, and are in particular blind to the local curvature of the decision boundary.
A Dynamical System Perspective for Lipschitz Neural Networks
The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples.
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
This thesis focuses on the problem of training neural networks which are not only accurate but also compact, easy to train, reliable and robust to adversarial examples.
Advocating for Multiple Defense Strategies against Adversarial Examples
It has been empirically observed that defense mechanisms designed to protect neural networks against $\ell_\infty$ adversarial examples offer poor performance against $\ell_2$ adversarial examples and vice versa.
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks.
The Expressive Power of Deep Neural Networks with Circulant Matrices
Recent results from linear algebra stating that any matrix can be decomposed into products of diagonal and circulant matrices has lead to the design of compact deep neural network architectures that perform well in practice.
Robust Neural Networks using Randomized Adversarial Training
This paper tackles the problem of defending a neural network against adversarial attacks crafted with different norms (in particular $\ell_\infty$ and $\ell_2$ bounded adversarial examples).
Theoretical evidence for adversarial robustness through randomization
This paper investigates the theory of robustness against adversarial attacks.
Understanding and Training Deep Diagonal Circulant Neural Networks
In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones.
Training compact deep learning models for video classification using circulant matrices
In real world scenarios, model accuracy is hardly the only factor to consider.
