影像隱寫(Image Steganography)是將訊息隱藏入於影像中以進行秘密通訊，即利用載體影像做為偽裝來避免外人察覺，接收方則可由該影像中擷取秘密訊息。為了傳遞大量訊息，所隱藏的內容可同為影像，即以圖藏圖的應用。現存的影像隱寫技術雖然可將幾乎同樣大小的秘密影像嵌入於載體影像中而不產生明顯失真，並可擷取出完整秘密影像，但通常未考量傳輸影像時必要的有損壓縮，例如將影像以最常見的JPEG格式儲存及傳輸，失真壓縮可能導致影像隱寫失敗。為了避免JPEG壓縮對於秘密訊息的影響，我們提出一個符合JPEG壓縮邏輯的影像隱寫模型JSN (JPEG Steganography Network)。JSN運用可逆神經網路做為深度學習模型架構主幹，結合JPEG編碼流程，對影像施予8×8離散餘弦轉換並考量JPEG所規範的量化步階，使載體影像在嵌入秘密影像後能有效降低JPEG有損壓縮的影響。可逆神經網路的使用讓JSN在嵌入過程與擷取過程使用共同的架構與參數，除了維持載體影像與秘密影像畫質外，在嵌入後的額外量化程序能在訓練擷取網路參數的同時也能影響嵌入網路參數。我們對JSN進行廣泛測試，實驗結果證實JSN能夠取得良好的影像隱寫效果，並符合相關應用的實際需求。;Image Steganography is the technique of hiding messages within images for secret communication, using the carrier image as a disguise to avoid detection by outsiders. The recipient can then extract the hidden message from the stego image. To transmit a large amount of information, the hidden content can also be an image, leading to applications where images are hidden within images. Although existing image steganography techniques can embed nearly the same-sized secret image into a carrier image without significant distortion and can extract the complete secret image, they often do not account for necessary lossy compression during transmission, such as when saving and transmitting images in the commonly used JPEG format. Lossy compression can lead to the failure of image steganography. To mitigate the impact of JPEG compression on secret messages, we propose an image steganography model called JSN (JPEG Steganography Network) that aligns with JPEG compression. JSN utilizes a reversible neural network as the backbone of the deep learning model, combined with the JPEG encoding process. It applies an 8×8 Discrete Cosine Transform (DCT) and takes account of the quantization steps specified by JPEG, ensuring that the impact of JPEG lossy compression on the stego image be reduced. The use of a reversible neural network allows JSN to use the same architecture and parameters during both the embedding and extraction processes. In addition to maintaining the quality of both the stego and secret images, the additional quantization process after embedding influences both the embedding and extraction network parameters during training.
We have conducted extensive testing on JSN, and the experimental results confirm that JSN achieves excellent image steganography performance and meets the practical needs of related applications.
