Unlike hiding bit-level messages, hiding image-level messages is more challenging, which requires large capacity, high imperceptibility, and high security. Although recent advances in hiding image-level messages have been remarkable, existing schemes are limited to lossless spatial images as covers and cannot be directly applied to JPEG images, the ubiquitous lossy format images in daily life. The difficulties of migration are caused by the lack of targeted design and the loss of details due to lossy decompression and re-compression. Considering that taking DCT densely on $8\times8$ image patches is the core of the JPEG compression standard, we design a novel model called \textsf{EFDR}, which can comprehensively \underline{E}xploit \underline{F}ine-grained \underline{D}CT \underline{R}epresentations and embed the secret image into quantized DCT coefficients to avoid the lossy process. Specifically, we transform the JPEG cover image and hidden secret image into fine-grained DCT representations that compact the frequency and are associated with the inter-block and intra-block correlations. Subsequently, the fine-grained DCT representations are further enhanced by a sub-band features enhancement module. Afterward, a transformer-based invertibility module is designed to fuse enhanced sub-band features. Such a design enables a fine-grained self-attention on each sub-band and captures long-range dependencies while maintaining excellent reversibility for hiding and recovery. To our best knowledge, this is the first attempt to embed a color image of equal size in a color JPEG image. Extensive experiments demonstrate the effectiveness of our \textsf{EFDR} with superior performance.

翻译：不同于隐藏比特级信息，隐藏图像级信息更具挑战性，需要大容量、高隐蔽性和高安全性。尽管近年来隐藏图像级信息的研究取得了显著进展，但现有方案仅限于以无损空间图像为载体，无法直接应用于日常生活中广泛存在的有损格式图像——JPEG图像。迁移困难源于缺乏针对性设计以及有损解压缩和再压缩导致的细节损失。考虑到对$8\times8$图像块进行密集DCT变换是JPEG压缩标准的核心，我们设计了一种名为\textsf{EFDR}的新模型，能够全面利用细粒度DCT表示，并将秘密图像嵌入量化后的DCT系数中以避免有损过程。具体而言，我们将JPEG载体图像和隐藏秘密图像转换为细粒度DCT表示，该表示紧凑地反映了频率特性，并与块间和块内相关性相关联。随后，通过子带特征增强模块进一步强化细粒度DCT表示。接着，设计了基于Transformer的可逆性模块来融合增强后的子带特征。这种设计使得每个子带能够进行细粒度的自注意力机制，捕获长距离依赖关系，同时保持优异的隐藏与恢复可逆性。据我们所知，这是首次尝试在彩色JPEG图像中嵌入等尺寸彩色图像。大量实验证明了我们的\textsf{EFDR}模型的有效性及其优越性能。