EctFormer: High-Imperceptibility Deep Image Steganography Based on Empirical Mode Decomposition

Abstract

Image steganography, a crucial technique for secure information transmission, faces the challenge of balancing embedding capacity with visual imperceptibility and security. Existing methods often struggle to maximize these metrics simultaneously, particularly when handling complex image details and achieving adaptive feature representation. To address this, we propose EctFormer, a novel deep steganography framework based on Image Hiding Empirical Mode Decomposition (IHEMD). EctFormer employs a compact autoencoder architecture with a key innovation: an integrated IHEMD module that adaptively decomposes images into physically meaningful intrinsic mode functions (IMFs) and residual components. This decomposition allows for superior feature representation and information embedding. Furthermore, we introduce an intrinsic mode loss function within a novel multi-image training strategy, achieving a remarkable embedding capacity of 96 bits per pixel. Experimental results on the DIV2K, COCO, and ImageNet datasets demonstrate EctFormer's superior performance. Our method significantly improves PSNR (exceeding 17.00 dB for single-image tasks and 11.00 dB for multi-image tasks) while maintaining high SSIM values (above 0.99). These results surpass current state-of-the-art methods, validating the efficacy of our IHEMD-based approach and the proposed training strategy. EctFormer provides a new effective paradigm for image steganography and enables high-capacity, high-security covert communication. The code is available at https://github.com/lisen1129/EctFormer. © 2025 Elsevier B.V., All rights reserved.