An Explainable Deep Learning Framework for Detection of AI-Generated Images

Abstract

The rapid development of generative artificial intelligence has enabled the production of highly realistic synthetic images that are increasingly difficult to distinguish from authentic visual content. Although deep learning-based detection methods have demonstrated promising performance in identifying AI-generated images, many existing approaches operate as black-box models and provide limited explanations for their predictions. This lack of interpretability creates challenges for applications where users need to understand why an image has been classified as authentic or synthetic. This paper proposes an Explainable Deep Learning Framework for Detection of AI-Generated Images that combines deep visual feature learning with explainable artificial intelligence techniques. The proposed framework analyzes spatial and potentially frequency-domain characteristics of images, extracts discriminative features using a convolutional or transformer-based deep learning architecture, and classifies images into real and AI-generated categories. An explainability layer is incorporated using techniques such as Grad-CAM, saliency analysis, or related post-hoc explanation methods to identify image regions contributing to the model\'s decision. The framework is designed to evaluate not only classification performance but also robustness, generalization to unseen image-generation models, and interpretability. Existing literature indicates that cross-generator generalization remains a significant challenge because detector performance can decline when images originate from generators or scenes not represented during training. The proposed framework therefore emphasizes generalized detection and visual explanation rather than accuracy alone. The study is expected to contribute a transparent and reproducible approach for AI-generated image forensics and provide a foundation for trustworthy synthetic-media detection.

Introduction

The text proposes an Explainable Deep Learning Framework for AI-Generated Image Detection to address the growing challenges associated with realistic synthetic images produced by generative adversarial networks (GANs), diffusion models, and other artificial intelligence technologies. Although AI-generated images have useful applications in design, education, entertainment, and digital media, they can also facilitate misinformation, fraud, impersonation, and digital manipulation. Therefore, reliable methods for distinguishing authentic photographs from AI-generated images are increasingly important for digital forensics and multimedia security.

The research identifies several limitations of existing detection systems. Deep learning models can learn subtle visual patterns and artifacts that distinguish synthetic images from authentic photographs, but they may struggle with unseen image generators, compression, resizing, cropping, and other post-processing operations. Furthermore, many existing detectors provide only a classification result without explaining the evidence behind their decisions. These limitations reduce their transparency, reliability, and practical usefulness.

The primary aim of the proposed research is to develop a deep learning system that can classify images as Real or AI-Generated while providing interpretable visual explanations. Its main objectives include collecting a diverse dataset, developing a binary classification model, investigating spatial and frequency-domain features, integrating Explainable Artificial Intelligence (XAI), evaluating robustness, and comparing performance across different image generators.

The proposed methodology consists of several stages:

• Dataset Collection: Gather authentic and AI-generated images from multiple sources, including GAN-based and diffusion-based generators, to improve the diversity of training data.

• Image Preprocessing: Remove corrupted and duplicate images, resize and normalize images, apply optional data augmentation, and divide the dataset into training, validation, and testing subsets.

• Deep Learning Classification: Use architectures such as ResNet, EfficientNet, ConvNeXt, Vision Transformer, or Swin Transformer to extract image features and classify images as real or synthetic.

• Explainable AI Integration: Apply Gradient-weighted Class Activation Mapping (Grad-CAM) to generate heatmaps highlighting image regions that influence the model's predictions.

• Robustness Testing: Evaluate the detector under image compression, resizing, cropping, filtering, and other transformations.

• Performance Evaluation: Measure accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix results, while testing generalization to previously unseen image generators.

The key contribution is the integration of explainability into the image detection pipeline. Instead of merely predicting whether an image is synthetic, the system generates a visual heatmap that helps users understand which regions influenced the classification. This can assist researchers, journalists, digital forensic analysts, and content moderators in assessing the model's reasoning. However, the heatmap indicates influential regions rather than independently proving that an image is AI-generated.

The research also emphasizes five important challenges: limited cross-generator generalization, overreliance on classification metrics, insufficient integration of explainability, sensitivity to image post-processing, and difficulty detecting images produced by newer generative models. It therefore investigates the relationship between detection performance and explanation quality, alongside the robustness of predictions under changing conditions.

Conclusion

This paper proposed an Explainable Deep Learning Framework for Detection of AI-Generated Images. The framework addresses the growing challenge of distinguishing synthetic images from authentic visual content while recognizing that reliable detection requires more than classification accuracy. The proposed approach combines deep learning-based visual feature extraction, optional frequency-domain analysis, binary classification, cross-generator evaluation, robustness testing, and an Explainable AI layer. The explainability component is intended to provide visual evidence showing which regions contribute to the model\'s prediction. The research is particularly focused on addressing the challenges of generalization, robustness, and transparency. Existing literature indicates that detector performance can decline across unseen generators and domains, while explainability remains an important requirement for trustworthy synthetic-media detection. The proposed framework can therefore provide a foundation for developing transparent AI-generated image forensic systems. Its final effectiveness should be established through controlled experiments using diverse datasets, unseen generators, post-processing transformations, and quantitative as well as explainability-oriented evaluation.

References

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Copyright

Copyright © 2026 Sthitipragnya Sabat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.