With the rapid development of generative AI, image steganography has garnered widespread attention due to its unique concealment. Recent studies have demonstrated the practical advantages of Fixed Neural Network Steganography (FNNS), notably its ability to achieve stable information embedding and extraction without any additional network training. However, the stego images generated by FNNS still exhibit noticeable distortion and limited robustness. These drawbacks compromise the security of the embedded information and restrict the practical applicability of the method. To address these limitations, we propose Robust Fixed Neural Network Steganography (RFNNS). Specifically, a texture-aware localization technique selectively embeds perturbations carrying secret information into regions of complex textures, effectively preserving visual quality. Additionally, a robust steganographic perturbation generation (RSPG) strategy is designed to enhance the decoding accuracy, even under common and unknown attacks. These robust perturbations are combined with AI-generated cover images to produce stego images. Experimental results demonstrate that RFNNS significantly improves robustness compared to state-of-the-art FNNS methods, achieving an average increase in SSIM of 23\% for recovered secret images under common attacks. Furthermore, the LPIPS value of recovered secrets images against previously unknown attacks achieved by RFNNS was reduced to 39\% of the SOTA method, underscoring its practical value for covert communication.

翻译：随着生成式人工智能的快速发展，图像隐写术因其独特的隐蔽性而受到广泛关注。近期研究表明，固定神经网络隐写术（FNNS）具有显著的实际优势，特别是其无需额外网络训练即可实现稳定的信息嵌入与提取。然而，FNNS生成的隐写图像仍存在明显的失真和有限的鲁棒性。这些缺陷会危及嵌入信息的安全性，并限制该方法的实际适用性。为解决这些局限性，我们提出了鲁棒固定神经网络隐写术（RFNNS）。具体而言，一种纹理感知定位技术选择性地将携带秘密信息的扰动嵌入复杂纹理区域，有效保持了视觉质量。此外，设计了鲁棒隐写扰动生成（RSPG）策略，以提升解码准确率，即使在常见及未知攻击下也能保持性能。这些鲁棒扰动与AI生成的载体图像结合，产生隐写图像。实验结果表明，与最先进的FNNS方法相比，RFNNS显著提升了鲁棒性，在常见攻击下恢复的秘密图像SSIM平均提高了23%。此外，RFNNS在应对先前未知攻击时，恢复的秘密图像LPIPS值降至SOTA方法的39%，凸显了其在隐蔽通信中的实用价值。