Recently, deep learning-based techniques have undergone rapid development, yielding promising results in various fields. For making more complex operations in day-to-day tasks, the arbitrary resolution of JPEG image data security requires more than just deep learning in this modern era. To overcome this, our research introduces a pioneering synergistic framework for a quantum-resistant deep learning technique, which is expected to provide next-generation robust security in the dynamic resolution of multi-JPEG-image-based joint compression-encryption. Our proposed framework features dual-parallel processing of a dynamic gate network, utilizing a convolutional neural network for specialization detailing and quantum-inspired transformations. These transformations leverage Riemann zeta functions for depth feature extraction, integrated with a chaotic sequence and dynamic iterations to generate a latent-fused chaotic key for image joint compression and encryption. Further, the authenticity of an encrypted image that is bound by a secure pattern derived from a random transform variance anchors cryptographic operations. Then, bound data transmitted through a Synergic Curve Key Exchange Engine fused with renowned Chen attractors to generate non-invertible keys for transmission. Finally, experimental results of the image reconstruction quality measured by the structural similarity index metric were 98.82 1.12. Security validation incorporates different metrics by addressing the entropy analysis to quantify resistance against differential and statistical attacks, with a yield of 7.9980 0.0015. In conclusion, the whole implementation uniquely combines latent-fused chaotic with improved key space analysis for discrete cosine transform quantization with authenticated encryption, establishing an adversarial-resistant pipeline that simultaneously compresses data and validates integrity through pattern-bound authentication
Gangappa et al. (Mon,) studied this question.