DGEN: A Dynamic Generative Encryption Network for Adaptive and Secure Image Processing
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Abstract
Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public image sets. Results show DGEN beats AES, chaos tricks, and other GAN ideas. Entropy reached 7.99 bits per pixel, correlation dropped 0.002, and the avalanche effect was 95.4 percent. Encrypting a surveillance frame took 7.5 ms, while the picture quality stayed high, with PSNR 39.7 dB and SSIM 99.2. These numbers suggest the tool can still work in real time and scale up significantly. The study also looks at how DGEN could fit with quantum computers and federated learning, hinting it might be a very big step forward for safe image handling.
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