Worst-Case Morphs: a Theoretical and a Practical Approach

使用 Wasserstein ALI 和改进的 MIPGAN 的最坏情况变形

Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN

提出一种基于 Wasserstein ALI 的变形生成方法，在未知人脸识别系统下近似最坏情况变形，并通过改进 MIPGAN 提升攻击挑战性。

ChatGPT遭遇变形攻击检测：基于多模态大语言模型与通用视觉模型的零样本MAD

ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Models

本文提出基于通用视觉模型与GPT-4 Turbo等多模态LLM的两类零样本MAD方法，并在打印-扫描变形数据集上验证其检测准确率、泛化性及可解释优势。

A Practical Upper Bound for the Worst-Case Attribution Deviations

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks; however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper b…

Probabilistically Robust Learning: Balancing Average- and Worst-case Performance

Many of the successes of machine learning are based on minimizing an averaged loss function. However, it is well-known that this paradigm suffers from robustness issues that hinder its applicability in safety-critical domains. These issues are often addressed by training against worst-case perturbations of data, a technique known as adversarial training. Although empirically effective, adversarial training can be overly conservative, leading to unfavorable trade-offs between nominal performance and robustness. To this end, in this paper we propose a framework called probabilistic robustness th…

Worst-case Optimal Submodular Extensions for Marginal Estimation

Submodular extensions of an energy function can be used to efficiently compute approximate marginals via variational inference. The accuracy of the marginals depends crucially on the quality of the submodular extension. To identify the best possible extension, we show an equivalence between the submodular extensions of the energy and the objective functions of linear programming (LP) relaxations for the corresponding MAP estimation problem. This allows us to (i) establish the worst-case optimality of the submodular extension for Potts model used in the literature; (ii) identify the worst-case …

MaxMatch: Semi-Supervised Learning with Worst-Case Consistency

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this paper. Specifically, we first pr…