An Explainable AI Framework for Image Analytics and Synthetic Image Creation Using CNN and GAN Architectures

International Journal of Computer Science and Engineering

© 2026 by SSRG - IJCSE Journal

Volume 13 Issue 2

Year of Publication : 2026

Authors : Gaurav Shekhar

10.14445/23488387/IJCSE-V13I2P101

How to Cite?

Gaurav Shekhar, "An Explainable AI Framework for Image Analytics and Synthetic Image Creation Using CNN and GAN Architectures," SSRG International Journal of Computer Science and Engineering , vol. 13,  no. 2, pp. 1-9, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I2P101

Abstract:

Explainable Artificial Intelligence (XAI) has become an important field of study to enable the exploration of interpretability and transparency issues relating to deep learning models, especially in high-stakes image analytics systems like medical imaging, surveillance, autonomous systems, and industrial inspection. Convolutional Neural Networks ( CNNs ) have been shown to outperform other state-of-the-art models in image classification, image detection, and image segmentation, and Generative Adversarial Networks (GANs) have been pioneers in generating synthetic images and data augmentation. Although successful, the CNN and GAN architectures are frequently criticized as black-box models that restrict the trust of users and regulatory requirements and implementation in sensitive devices. This paper will introduce a single Explainable AI system, which determines explainability mechanisms in CNN-based image analytics and GAN-based image generation. The framework also presented model-level, feature-level, and instance-level interpretability of CNN classifiers through gradient-based attribution, concept activation vectors, and saliency-based analysis of attention. Meanwhile, explainability is inherent in GAN models by discussing latent space representations, generator-discriminator dynamics, as well as the semantic disentanglement of generated elements. The framework permits transparency in predictive decisions, as well as in the generative mechanisms supporting the synthetic creation of images. A modular pipeline is made so that it enables interpretability throughout training, inference, and synthetic data generation phases. Mathematical formulations of CNN feature attenuation and GAN latent variable sensitivity analysis are introduced to give a theoretical basis. Benchmark image datasets are evaluated experimentally to evaluate the accuracy of classification, generative fidelity, metrics of explainability, and human interpretability scores. Findings indicate that the proposed framework is highly effective in enhancing model transparency without affecting the predictive performance or synthetic image quality. This work has made the following contributions: (i) a single explainable architecture with both CNN and GAN models, (ii) formal explainability measures of generative models, as well as (iii) a scalable framework applicable to practical image analytics systems. The study develops credible AI bridging performance and interpretability in contemporary deep learning-based image systems.

Keywords:

Explainable Artificial Intelligence, Convolutional Neural Networks, Generative Adversarial Networks, Image Analytics, Synthetic Image Generation, Model Interpretability, Trustworthy AI.

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