<![CDATA[The large scale exchange of digital images requires security mechanisms that are robust not only at the cryptographic algorithm level but also in the key generation process, which is often the weakest component of the system. In conventional ElGamal schemes, security may degrade due to static entropy sources and predictable key patterns. This study proposes an ElGamal key generation model based on a pipeline of Convolutional Neural Networks (CNNs) and a rolling hash function, utilizing visual image content as an adaptive entropy source. The CNN extracts latent features through a fully connected layer, while the rolling hash enhances diffusion and key sensitivity to minor image variations. The model was evaluated using the CIFAR-10 dataset in PNG, WEBP, and JPG formats. Experimental results show stable key generation times ranging from 0.426 to 0.444 ms, with high entropy values between 7.98 and 7.99 bits, indicating strong randomness and resistance to prediction. Strong diffusion characteristics were also observed (PSNR 5.94 dB, SSIM −0.24, MAE 0.43). During encryption, WEBP achieved the fastest processing time (0.48 ms), followed by PNG (1.01 ms) and JPG (15.39 ms), while PNG demonstrated the highest size efficiency with a reduction of up to 70.6%. Decryption remained highly reliable, with success rates exceeding 97% across all formats. Overall, the results confirm that integrating CNNs and rolling hash significantly enhances ElGamal key generation security without compromising decryption reliability or image quality.]]>
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