Learning to Secure: Image Encryption and Decryption with Artificial Neural Networks
Author(s): MANISH KUMAR
Authors Affiliations:
Assistant Professor, Department of Computer Applications, Swami Vivekanand Group of Institutes, Banur, Punjab, India
DOIs:10.2015/IJIRMF/202506054 | Paper ID: IJIRMF202506054In a world increasingly defined by pixels and patterns, safeguarding digital images has become both a technical imperative and a societal necessity. This research presents a forward-looking exploration into the use of Artificial Neural Networks (ANNs) for image encryption and decryption, proposing not just an algorithmic alternative to traditional cryptography, but a new philosophy of learning to secure. Unlike deterministic methods like AES or RSA, ANNs learn the art of obfuscation, generating encryption schemes as fluid and dynamic as the data they protect. Through architectures such as CNNs, RNNs, and autoencoders, our models encode images into unpredictable ciphertexts while retaining the capability to accurately decode them, even under noise, compression, and partial data loss. Experimental validation across datasets like MNIST, CIFAR-10, and high-resolution real-world images reveals high entropy, low correlation, and PSNR/SSIM values that rival or surpass classical approaches. More than metrics, however, this paper investigates the ANN’s capacity to resist plaintext and ciphertext-only attacks, adapt to diverse modalities, and function in resource-constrained environments like IoT and satellite systems. What emerges is a vision of encryption that is intelligent, resilient, and evolvable—one where the model learns not only to protect but to adapt and recover. As quantum computing and ethical ambiguity threaten to outpace conventional cryptography, we argue for a future where encryption is not just a locked box, but a living, learning shield. This research lays the groundwork for such systems, blending security with cognition and carving a new path for AI in cryptographic innovation.
Manish Kumar (2025); Learning to Secure: Image Encryption and Decryption with Artificial Neural Networks, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-6, Pp.431-441.     Available on –  https://www.ijirmf.com/
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