Designing Steganographic Codes Based on PAC Codes to Enhance Steganographic Security in Short-Cover Scenarios

Steganography is an important branch of information hiding used for covert communication. In modern steganography, steganographic coding is the key technology for content-adaptive message embedding, achieving near-optimal security performance by minimizing distortion. So far, Syndrome-Trellis Codes (STC) and Steganographic Polar Codes (SPC) have demonstrated near-optimal performance in content-adaptive steganography. However, when the cover is short, both of these codes show a significant performance gap from the rate-distortion bound. To address this limitation, we propose Steganographic Polarization-Adjusted Convolutional Codes (SPACC), a novel near-optimal steganographic coding method tailored for short cover scenarios. SPACC innovatively integrates Polarization-Adjusted Convolutional (PAC) codes, which have shown better performance than traditional polar codes in short block-length communication scenarios. This advantage motivates us to adopt PAC codes in steganographic coding. Specifically, SPACC uses the polarization channel metrics calculated by Bhattacharyya parameters to select the parity-check matrix for steganographic coding. Then, secret messages are embedded using the Successive Cancellation List (SCL) decoder of PAC codes while minimizing the total distortion. Experimental results demonstrate that our proposed SPACC achieves performance much closer to the optimal coding for short cover. Even for longer cover, it outperforms SPC in coding efficiency.