A New Kind of Adversarial Example: Measuring the Human-Model Gap, and Its Relationship to OOD Detection

Adversarial Attacks on Deep OCR Systems

Deep-OCR (DeepSeek-OCR) advances document recognition by treating the visual modality as an optical compression medium, enabling long-context OCR at low token cost. However, its increased complexity may introduce new security vulnerabilities. In this paper, we present, to the best of our knowledge, the first pure black-box adversarial attack against a generative OCR vision-language model, where only the decoded string can be queried and no gradients, logits, or model internals are available. We recast the attack as a zeroth-order optimization problem driven by a bounded scalar loss defined directly on the string output via sequence similarity, and estimate the gradient with a random-direction finite-difference scheme whose query cost is independent of the image dimension. An Adam update with ell_infinity projection yields imperceptible perturbations for both untargeted and targeted objectives. Pilot experiments on Deep-OCR validate the string-only attack and evaluation pipeline and expose severe qualitative decoder failures, including repetition, truncation, and prompt leakage. They also show that controlled targeted rewriting remains substantially harder than untargeted degradation; we avoid claiming targeted success until the pre-registered evaluation is complete.

From Detection to Deception: Are AI-Generated Image Detectors Adversarially Robust?

What Makes Adversarial Examples Transfer Across Deepfake Detectors?

Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred to a target model, unknown to the attacker. Yet how source--target compatibility shapes attack success remains poorly understood. Prior studies evaluate limited detector pools and rarely disentangle architectural from training factors. We conduct a controlled evaluation of adversarial transferability across 60 detectors spanning six backbones, two pretraining regimes, and five training-data configurations, using two attack procedures: AutoAttack (AA) and the Carlini--Wagner attack with Expectation over Transformation (CW--EOT). Matched comparisons reveal significantly higher transfer when source and target share an exact backbone, architecture family, pretraining regime, or training data. This compatibility structure is attack-dependent: exact backbone compatibility has the largest effect under AA, whereas shared pretraining and training data have the largest effects under CW--EOT. When transfer is averaged across non-target sources, mean attack success rate (ASR) is $7.21\%$ under AA and $19.52\%$ under CW--EOT. By contrast, a multi-source oracle combining both attacks attains a \(64.48\%\) mean ASR after excluding exact backbone and training-data matches, showing that source averaging can substantially understate target vulnerability. We release 240,000 adversarially perturbed images, complete pairwise transfer results, detector configurations, and evaluation code. These findings establish source--target compatibility and source-model selection as central dimensions of credible transfer-based black-box robustness evaluation.

Rethinking Fake Adversarial Examples for Single-Step Adversarial Training

Adversarial training (AT) is a widely adopted defense against adversarial attacks, but its multi-step optimization process for generating adversarial examples leads to substantial computational overhead. To mitigate this, various single-step adversarial training methods have been proposed. However, these models often suffer from catastrophic overfitting under larger perturbations and exhibit degraded robustness. The core issue is that certain single-step adversarial examples, although successfully learned and correctly classified, fail to expose the true vulnerabilities of models. We refer to these misleading examples as “fakers”. Specifically, we find that fakers exhibit three distinct characteristics compared to standard adversarial examples: 1) they unexpectedly degrade the model robustness rather than improve it; 2) they make it harder for the model to learn their robust features; and 3) they show significantly greater divergence from their clean counterparts. These observations motivate us to proactively reduce the impact of fakers during training. To this end, we propose the Faker-Alleviating Single-step adversarial Training method (FAST), a general and effective framework designed to enhance both accuracy and robustness. Concretely, FAST consists of two main components. First, it dynamically adjusts the label-smoothing level for adversarial examples according to their learning difficulty, making fakers easier for the model to learn. Second, it introduces an auxiliary sample with a weak adversarial effect, derived from the single-step adversarial example, which is used to dynamically ease the alignment with clean data and stabilize the optimization process. We demonstrate the effectiveness of FAST through extensive experiments, showing that our method achieves superior clean accuracy and robustness against various types of adversarial attacks. The code is available at https://github.com/mesunhlf/FAST.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

This paper introduces the first attack that directly optimizes an encoder-attention objective under an imperceptible, bounded, bounded perturbation, and argues that encoder attention concentrates the model's spatial reasoning, so corrupting it propagates through the detection pipeline more disruptively than perturbing the detection output alone.

A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization

This survey examines a recent class of adversarial efficiency degradation attacks that target these mechanisms to increase computation without necessarily degrading accuracy, and unify and compare two representative attacks across three popular token-pruning frameworks.