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arXiv Machine Learning

Detecting Deceptive Recruitment: A Signal-theoretic Machine Learning Framework for Early Identification of Labour Exploitation

The paper presents a signal-theoretic machine learning framework to detect deceptive online job ads that facilitate forced labour. Using 464 verified cases from nine countries and 21 industries, the authors build multimodal models that combine computer vision, natural language processing, and semantic embeddings, achieving ROC‑AUC scores between 0.87 and 0.97. SHAP analysis identifies text quality, risk language, and visual features as key discriminators, and the authors deliver a proof‑of‑concept decision support system that outputs interpretable risk scores for practitioners.

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arXiv AI
Sep 18

FORGE: Forensic Reasoning with Grounded Evidence

FORGE is a forensic deepfake analysis system that provides region‑grounded natural language explanations for image manipulations. It addresses the inductive bias mismatch of multimodal large language models by adding a Vision‑Only Model trained on dense patch prediction, allowing the language model to interleave tokens with preserved spatial correspondence. Across face‑manipulated and fully synthetic content, FORGE delivers fine‑grained attribute queries and outperforms in‑domain baselines, with region‑specific evaluation and human studies confirming explanation faithfulness.

By Rohit Kundu, Shan Jia, Vishal Mohanty, Athula Balachandran, Amit K. Roy-Chowdhury
llmsroboticsmultimodalsafety
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arXiv Computation and Language
Sep 18

FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool

FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.

By Giovanni Spitale, Federico Germani
llms
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arXiv Computation and Language
Aug 24

Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift

The paper introduces a new evaluation setting called scenario‑level out‑of‑distribution (SL‑OOD) detection for SMS and voice phishing, where entire attack scenarios are omitted from training while the label space stays fixed. It shows that high in‑distribution performance does not guarantee robustness to unseen scenarios, attributing this to scenario memorization. The authors propose ECoG, an evidence‑consistent generative framework that uses evidence‑span supervision and a rationale‑label consistency objective, achieving notable improvements in Macro‑F1, reduced prediction‑rationale inconsistency, and higher token‑level overlap with reference evidence.

By San Kim, JinYeong Bak
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