High-Capacity Image Steganography Based on Improved DenseNet

Steganography is the art of hiding secret messages within a cover image, making it difficult for others to detect its presence. In this article, the authors propose a new method for high-capacity image steganography based on an improved version of the FCDenseNet deep learning model. They aim to overcome the limitations of existing techniques by introducing a novel pipeline for secret image generation and embedding, which ensures that the secret message is not compromised during the embedding process.
The authors analyze the outputs of the secret pipeline and observe that the deviation of each xi from the secret image increases with the increment of i. To quantify these deviations, they calculate the distances/similarity between the secret image and each xi using metrics such as Average Pixel Distance (APD), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Model (SSIM). These findings indicate a progressive divergence of xi from the secret image with increasing i, indicating that the information content of the secret image decreases as it progresses through the secret pipeline.
The authors also compare their method with existing techniques and demonstrate superior performance in terms of embedding capacity and robustness against attacks. They show that their approach can generate high-quality images with a large embedding capacity while maintaining a good balance between the quality of the cover image and the embedded message. Furthermore, they introduce a decay weight mechanism to improve the model’s robustness against attacks by adding noise to the embedded message.
To better understand their method, the authors use an analogy from cryptography: "Just as a lock can only be opened with the correct key, our approach uses a secret pipeline to generate a cover image that can only be unlocked by the correct decoding algorithm." In summary, the authors propose a novel method for high-capacity image steganography based on an improved version of FCDenseNet, which ensures the secrecy of the embedded message and outperforms existing techniques in terms of embedding capacity and robustness.