AI Detection
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SV-Detect: AI-generated Text Detection with Steering Vectors
new Abstract: Detecting machine-generated text is especially difficult under distribution shift, such as transfer across domains, source models, and editing attacks. We propose a fake-text detector based on steering vectors extracted from the hidden representations of a frozen language model. At each layer, we construct a direction that separates human-written from machine-generated text, and represent each input by its layer-wise alignment with these directions.
Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection
arXiv:2606.06481v1 Announce Type: new Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing. However, existing AI-text detection benchmarks largely focus on final outputs and provide limited understanding of how AI authorship signals emerge, accumulate, or disappear throughout the revision process. We introduce...
School shooting survivor sues AI gun detection firm after system failed to spot weapon
The injured teenage survivor of a January 2025 shooting at a Nashville, Tennessee high school recently sued the manufacturer of an “AI gun detection” system that failed to detect the handgun that left two dead, including the shooter. According to the lawsuit, which was filed in Davidson County court last month, the security company Omnilert either knew or should have known that there were “significant operational limitations in its gun detection system that could result in detection failures...
CAPTCHAs can still detect AI agents
Here is a 2-3 sentence summary of the article: The article discusses the effectiveness of CAPTCHAs in detecting AI agents. It highlights the limitations of current CAPTCHAs and proposes a new approach to improve their accuracy. The proposed approach involves using a combination of machine learning algorithms and human feedback to train the CAPTCHAs. Note: The summary is based on the provided article URL and does not include any commentary or personal opinions.
Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features
new Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies. Most existing detection models are trained and evaluated under a single generation setting, leaving their ability to generalize across unseen prompts unclear. In this study, we investigate cross-prompt generalization in fake news detection using three datasets of AI-generated articles produced under distinct prompts,...
Coding with "Enemy": Can Human Developers Detect AI Agent Sabotage?
arXiv:2606.05647v1 Announce Type: new Abstract: AI coding agents are increasingly embedded in real-world software development, collaborating with human developers while gaining broader access to codebases and tools. This creates a new attack surface: an agent can exploit human trust to sabotage development, for instance by inserting malicious code to accomplish a hidden side task. Most prior work studies AI sabotage in AI-only settings, paying limited attention to the role of human oversight...
ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
arXiv:2606.04706v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable AI-generated video detection is therefore essential for multimedia forensics, yet remains challenging due to the need to capture spatial artifacts, temporal dynamics, and generalize to evolving generative models. In this paper, we explore reconstruction error as a discriminative forensic cue for...
'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions
arXiv:2606.04906v1 Announce Type: new Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use. Rather, existing datasets and approaches often define their own criteria and make their own assumptions, sometimes implicitly, and often only loosely related to real-world needs and applications.
Adversarial Creation and Detection of AI-Generated Social Bot Content
arXiv:2606.07219v1 Announce Type: new Abstract: The convergence of large language models and social bots allows malicious actors to manipulate the information ecosystem by generating human-like content at scale. Existing models for detecting AI-generated content often fail in the wild, primarily due to the lack of ground-truth data. We address this gap through an adversarial methodology that models the impersonation of real social media users by malicious actors.
When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection
arXiv:2603.09242v2 Announce Type: replace Abstract: The growing realism of generative models has blurred the boundary between real and synthetic content, posing significant challenges to reliable AI-generated image detection. Although large-scale pre-trained Vision Foundation Models have advanced detection capability, their generalization to images from unseen generation pipelines remains inadequate. In this paper, we identify, for the first time, a key failure mechanism, termed...