Privacy-Preserving CNN Inference for Image Super-Resolution Cross Multiple Ciphertexts

Online image super-resolution (SR) services have been widely used in applications such as Remini and DeepAI. However, the exposure of plaintext images raises serious privacy concerns. While secure CNN inference techniques are employed to protect images in image classification, they are not applicable to the unique challenges posed by image SR: the output resolution is significantly higher than that of the input image. In this paper, we present a secure CNN inference scheme for image SR by employing a multiple ciphertext encapsulation method. We begin by designing fundamental homomorphic operations, including addition, multiplication, and rotation across ciphertexts. Recognizing that image SR typically involves an upsampling layer-unlike image classification-we propose a fast algorithm for secure upsampling. This technique leverages pre-weight block masking and cross-ciphertext rotation, resulting in a significant speedup compared to direct homomorphic upsampling. We then present an efficient batched homomorphic two-dimensional convolution method across ciphertexts, incorporating kernel rearrangement and merging strategies. We also design a polynomial activation function specifically optimized for image SR, further enhancing performance. Extensive experiments demonstrate that our HE-friendly SR network outperforms existing secure solutions, while the proposed multiple ciphertext encapsulation technique achieves at least a 2x improvement in both computational efficiency and memory usage.