mlsanityData to 14 Oct 2026

AI-Generated Image Recognition via Fusion of CNNs and Vision Transformers

arXiv 2606.27637 ↗First posted 26 Jun 2026cs.CVcited by 0

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Abstract (arXiv)

Recent advancements in synthetic data technology have opened a new era where images of remarkable quality are generated, blurring the lines between real-life images and those produced by Artificial Intelligence (AI). This evolution poses a significant challenge to ensuring the reliability and authenticity of data, underscoring the need for robust detection methods. In this paper, we present a robust approach aimed at addressing these pressing concerns. Our methodology revolves around leveraging fusion strategies, combining the strengths of multiple detection methods for identifying AI-generated images. Through extensive experimentation on the CIFAKE dataset, our model showcases remarkable performance, achieving an impressive accuracy rate of 97.32%. This accomplishment underscores the efficacy of our approach in accurately distinguishing between AI-generated images and real-life images, thus contributing to the advancement of data authentication techniques amidst the proliferation of synthetic data.

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References · 19

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  4. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  5. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 931
  6. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  7. 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 504
  8. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 490
  9. 2022Understanding Diffusion Models: A Unified Perspectivecited by 340
  10. 2020What makes fake images detectable? Understanding properties that generalizecited by 273
  11. 2018Learning Rich Features for Image Manipulation Detectioncited by 154
  12. 2019Detecting GAN generated Fake Images using Co-occurrence Matricescited by 124
7 more
  1. 2023CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Imagescited by 84
  2. 2019Semi-Supervised Learning with Normalizing Flowscited by 76
  3. 2019Detecting Photoshopped Faces by Scripting Photoshopcited by 55
  4. 2023Online Detection of AI-Generated Imagescited by 55
  5. 2019Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Functioncited by 26
  6. 2021A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervisioncited by 19
  7. 2020Random autoregressive models: A structured overviewcited by 2

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