Pith. sign in

REVIEW 5 cited by

Scalable Framework for Classifying AI-Generated Content Across Modalities

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.00375 v2 pith:2TKUT2VT submitted 2025-02-01 cs.CV

Scalable Framework for Classifying AI-Generated Content Across Modalities

Anh-Kiet Duong , Petra Gomez-Kr\"amer This is my paper
classification cs.CV
keywords generativeai-generatedclassifyingcontentframeworkacrossdefactify4distinguishing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The rapid growth of generative AI technologies has heightened the importance of effectively distinguishing between human and AI-generated content, as well as classifying outputs from diverse generative models. This paper presents a scalable framework that integrates perceptual hashing, similarity measurement, and pseudo-labeling to address these challenges. Our method enables the incorporation of new generative models without retraining, ensuring adaptability and robustness in dynamic scenarios. Comprehensive evaluations on the Defactify4 dataset demonstrate competitive performance in text and image classification tasks, achieving high accuracy across both distinguishing human and AI-generated content and classifying among generative methods. These results highlight the framework's potential for real-world applications as generative AI continues to evolve. Source codes are publicly available at https://github.com/ffyyytt/defactify4.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Findings of the Counter Turing Test: AI-Generated Image Detection

    cs.CV 2026-05 unverdicted novelty 4.0

    The Counter Turing Test competition finds F1-scores above 0.83 for binary real-vs-AI classification but only 0.4986 at best for identifying the specific generative model.

  2. Findings of the Counter Turing Test: AI-Generated Image Detection

    cs.CV 2026-05 unverdicted novelty 4.0

    A competition using a new 50k-image dataset found high accuracy in binary real-vs-AI detection but only modest success in identifying the exact generative model.

  3. Findings of the Counter Turing Test: AI-Generated Image Detection

    cs.CV 2026-05 unverdicted novelty 3.0

    Binary AI vs. real image classification reaches F1 > 0.83 while identifying the exact generative model achieves a highest F1 of 0.4986 on the MS COCOAI dataset.

  4. Findings of the Counter Turing Test: AI-Generated Text Detection

    cs.CL 2026-05 unverdicted novelty 2.0

    Shared task findings show F1=1.0000 for binary AI text detection and 0.9531 for model attribution using fine-tuned DeBERTa and BART transformers with ensembles.

  5. Findings of the Counter Turing Test: AI-Generated Text Detection

    cs.CL 2026-05 unverdicted novelty 2.0

    Shared task findings show near-perfect binary detection of AI-generated text but greater difficulty in attributing outputs to particular language models.