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@article{172383,
        author = {Atharva Kulkarni},
        title = {Neural Network-Driven Cryptographic Frameworks: Enhancing Image Security Through AI-Based Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3059-3076},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172383},
        abstract = {In an era where the proliferation of digital imagery over insecure networks grows exponentially, robust cryptographic systems are essential to safeguard sensitive visual data. This paper introduces an innovative cryptographic framework leveraging artificial neural networks (ANNs) to enhance image encryption and security. The proposed system integrates machine learning and advanced cryptographic algorithms to achieve superior resistance against traditional and emerging cyber threats. We evaluate the system's performance using Structural Similarity Index Measure (SSIM), entropy, and computational efficiency. Experimental results demonstrate significant advancements in encryption strength, efficiency, and resilience against statistical and differential attacks, showcasing the potential of neural network-driven systems to redefine standards in image security.},
        keywords = {Neural Networks, Image Encryption, Cryptographic Systems, Machine Learning, Data Security, Artificial Intelligence, Structural Similarity Index Measure (SSIM), Statistical Attack Resistance, Differential Attack Resistance, Advanced Cryptography.},
        month = {January},
        }