TrueSightQ: A Multimodal Framework for Detecting AI-Generated Content Using Quantum Techniques and Web3 Integration

Abstract

The rapid progress of generative AI has already seen the rise of highly realistic artificial and deepfake content that has created a problems related to trust on information, privacy, issues related to cyber security loss of general trust. This research introduces TrueSightQ, a unified full stack web application framework through with multimodal detection of AI generated content through use of Quatnum enhancement and Web3 Integration. This system is a hybrid between heuristic and deep learning methods with the added feature of GPU accelerated training combined with quantum advantage classifiers. The trustworthiness and transparency of blockchain technology, as well as IPFS storage and Ethereum Smart contract to improve verification process. The model is further enhanced with modality wise fusion and, decentralized trust based mechanisms for the defense mechanism to adversarial attack. Results show that in general TrueSightQ significantly outperforms standard unimodal detectors overall, with additional gains to verifiability, precision and interpretability demonstrating how the multimodal and decentralized methodology within the model mitigates the issues of AI generated content very efficiently.