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@article{200165,
        author = {Sumeet Hinge and Ranjeet Patil and Varad Virdhe and Aditya Mukati},
        title = {Attack Detection for Facial Recognition System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2036-2039},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200165},
        abstract = {This paper presents an enhanced facial recognition security system that integrates biometric authentication with real-time intrusion detection and multi- channel alert mechanisms. The system addresses critical security challenges in access control by combining powerful deep learning models, specifically Convolutional Neural Networks (CNNs), for accurate recognition with multi-modal anti-spoofing techniques to prevent fraudulent attempts [3, 13, 14]. A parallel Intrusion Detection System (IDS) monitors background processes, critical files, and network activity to identify and block cyberattacks, ensuring holistic security [4, 5]. The proposed solution provides comprehensive, real-time security monitoring with instant alerts delivered through multiple communication channels (email, sound, desktop notifications). This integrated system is specifically designed for deployment in high-security environments where both physical access  control and digital  system security are crucial for mitigating diverse security risks},
        keywords = {Facial Recognition, Anti-Spoofing, Intrusion Detection System, Biometric Security, Computer Vision, Deep Learning, Cybersecurity, Multi-Factor Authentication},
        month = {May},
        }