Pith. sign in

REVIEW 9 cited by

Detecting GAN generated Fake Images using Co-occurrence Matrices

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 1903.06836 v2 pith:6YGYMOT4 submitted 2019-03-15 cs.CV eess.IV

Detecting GAN generated Fake Images using Co-occurrence Matrices

Lakshmanan Nataraj , Tajuddin Manhar Mohammed , Shivkumar Chandrasekaran , Arjuna Flenner , Jawadul H. Bappy , Amit K. Roy-Chowdhury , B. S. Manjunath This is my paper
classification cs.CV eess.IV
keywords imagesapproachco-occurrencefakematricesachievesdatasetsdeep
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated methods have become increasingly popular in creating fake images. In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. We extract co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Experimental results on two diverse and challenging GAN datasets comprising more than 56,000 images based on unpaired image-to-image translations (cycleGAN [1]) and facial attributes/expressions (StarGAN [2]) show that our approach is promising and achieves more than 99% classification accuracy in both datasets. Further, our approach also generalizes well and achieves good results when trained on one dataset and tested on the other.

discussion (0)

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

Forward citations

Cited by 9 Pith papers

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

  1. LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection

    cs.CV 2026-07 conditional novelty 6.0

    A dual-stream deepfake forensic model that adds DDIM reconstruction residuals to RGB features improves artifact localization and cross-generator detection in evaluations, with honest caveats about text faithfulness.

  2. PhantomSeal: Proactive Deepfakes Defense with Identity/Context Protection and Forensic Tracing

    cs.CR 2026-07 conditional novelty 6.0

    A single perturbation can steer face-swap outputs toward a chosen 'cloak' identity, giving both identity/context protection and forensic tracing.

  3. Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection

    cs.CV 2026-07 conditional novelty 6.0

    Modeling the differential response of real versus AI-generated images under secondary generative transformations yields state-of-the-art cross-generator detection on 19 unseen models.

  4. DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection

    cs.CV 2026-04 unverdicted novelty 6.0

    DVAR turns video authenticity detection into an iterative debate between a generative hypothesis agent and a natural mechanism agent, resolved via minimum description length and a knowledge base for better generalizat...

  5. Deepfake Detection Generalization with Diffusion Noise

    cs.CV 2026-04 unverdicted novelty 6.0

    ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.

  6. Beyond Semantics: Uncovering the Physics of Fakes via Universal Physical Descriptors for Cross-Modal Synthetic Detection

    cs.CV 2026-04 unverdicted novelty 6.0

    Five universal physical descriptors including Laplacian variance, Sobel statistics, and residual noise variance, when integrated as text encodings with CLIP, achieve up to 99.8% accuracy detecting synthetic images acr...

  7. SPARK-IL: Spectral Retrieval-Augmented RAG for Knowledge-driven Deepfake Detection via Incremental Learning

    cs.CV 2026-04 unverdicted novelty 6.0

    SPARK-IL reaches 94.6% mean accuracy on deepfake detection across 19 generators by fusing multi-band spectral embeddings from ViT and RGB paths, retrieving nearest signatures for majority voting, and using incremental...

  8. Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection

    cs.CV 2024-11 unverdicted novelty 6.0

    Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generali...

  9. Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation

    cs.CV 2026-05 unverdicted novelty 4.0

    Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.