AI-Powered Stegnographic Image Authentication System

B R Rakshitha                                                       B R Rakshith; Manjula k

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Authors

B R Rakshitha B R Rakshith Student, Department of MCA, GM University
Author
Manjula k Assistant professor, GM University
Author

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Keywords:

AI Art Generation, Deepfake Detection, Steganography, Image Authentication, Cloud Security, React Native, XceptionNet

Abstract

With the rapid evolution of artificial intelligence (AI) and digital media, ensuring authenticity and ownership of AI-generated images has become increasingly critical. This paper presents INVIS-MARK, a cross-platform mobile framework that integrates AI art generation, steganography, and deepfake detection to establish secure, traceable, and authentic visual content. Developed using React Native, Node.js, Flask, and Cloudinary, the system allows users to generate or upload images, embed hidden metadata using the Least Significant Bit (LSB) algorithm, and verify authenticity through a TensorFlow-based XceptionNet deepfake detection model. Verified images are stored in the cloud with metadata securely maintained in Neon PostgreSQL. The research highlights the convergence of AI creativity and cybersecurity, demonstrating how INVIS-MARK prevents media forgery, plagiarism, and identity misuse in AI-generated digital ecosystems.