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@article{189784,
        author = {Archana J R and Chaithra S K and Kavana B M and Shreya N R and Nikita Kunachi},
        title = {Verity Vision: Holistic anti-spoofing and fabricated content recognition system implementing MobileNet and ResNeXt},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {518-523},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189784},
        abstract = {Verity Vision is a deep-learning anti-spoofing and content authentication system that detects fake or manipulated media. It couples the speed and real-time feature extraction capability of Mobile Net with the strong classification capability of ResNeXt to set apart genuine from spoofed inputs. It can defend against printed photos, replayed videos, 3D masks, and AI-generated deepfakes, making it suitable for secure biometrics, surveillance, and digital content verification. The system runs on mobile and cloud platforms for flexibility and scalability. It emphasizes security, reliability, and user-centric design with secure media handling, strict content validation, and finally, structured logging in order to prevent misuse and aid audits in banking, e-governance, and remote identity verification. Predictions and authenticity reports are presented through an intuitive web interface and API for uncomplicated integration. Its lightweight architecture, platform-agnostic and extensible, allows for continuous updates with new datasets and attack patterns to make it resilient against evolving spoofing and deepfake techniques.},
        keywords = {Anti-spoofing, Fake Media Detection, Hybrid CNN Architecture, Deepfake detection, Mobile Net, ResNeXt.},
        month = {January},
        }