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Abstract

The rapid advancement of sophisticated generative models has intensified the need for robust fake image detection systems. However, many existing benchmark datasets suffer from limited diversity in content types and generation techniques, constraining the generalization ability of detection models. To address these limitations, we introduce GenPix (Generalized Pixels), a comprehensive dataset encompassing over 80,000 images spanning diverse categories, including faces, objects, and scenes, generated by multiple state-of-the-art models such as Generative Adversarial Networks (GANs) and diffusion-based architectures. The dataset includes samples from different generation methods to ensure broad coverage of fake image characteristics.

GenPix provides a realistic evaluation environment that better reflects real-world detection challenges. We establish baseline performance metrics using an Adversarial Autoencoder (AAE) and demonstrate the dataset's utility for developing and evaluating fake image detection systems. The AAE achieves 80.65% F1-score on the full GenPix test set and high inference throughput (488 images/sec).These results show that even relatively simple architectures can achieve promising performance on GenPix, while highlighting areas for improvement in detection methodologies.In contrast, deeper CNNs such as EfficientNet-B3 reach higher F1-score of 98.01% but suffer from low throughput (14 images/sec), suggesting a complementary trade-off between performance and practicality. Overall, GenPix provides a challenging and realistic benchmark for evaluating modern detectors, and the proposed AAE offers an efficient, interpretable baseline for future research on general-purpose fake-image detection.

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