# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 2 Relate Work  
### Traditional Steganography Methods  
Classical steganographic methods operate directly in the spatial domain to encode data. Least Significant Bit (LSB) [3] steganography conceals information by replacing the least significant bits of an image's pixel RGB color components. [PERSON] et al. [20] proposed the Jsteg algorithm based on LSB technology, primarily replacing the least significant bits in the AC coefficients of the original image with secret information. However, as the amount of information increases, this method gradually reveals statistical characteristics, leading to the \"paired values\" phenomena between pixel values, making it difficult to resist RS [21] (Regular and Singular) analysis. To address this issue, [PERSON] et al. [22] proposed the F5 algorithm, which combines Hamming code with minimal image modification to embed information, effectively enhancing steganographic security and detection resistance. Additionally, the Pixel Value Differencing (PVD) technique utilizes brightness differences between adjacent pixels to hide information, reducing the \"paired values\" phenomenon and increasing information hiding capacity, though this method is vulnerable to histogram analysis attacks. Subsequently, scholars proposed many PVD-based steganographic algorithms [23; 24; 25], effectively improving the limitations of the PVD algorithm.  
Compared to spatial domain methods, transform domain methods typically demonstrate higher robustness and security by utilizing image characteristics in the transform domain. The DFT algorithm [5] employs frequency domain characteristics to embed information into image frequency components, enhancing information concealment. The DCT algorithm [6], as a core technology in JPEG compression, utilizes image energy concentration characteristics to embed information in transform coefficients, offering good steganographic performance and compression resistance. The DWT algorithm [7] leverages multi-scale characteristics based on wavelet decomposition to embed information by analyzing local image features. These methods significantly improve steganographic concealment and security by fully utilizing image characteristics in the transform domain.  
In recent years, distortion function-based steganographic methods have further advanced technological development. [PERSON] et al. [9] proposed Highly Undetectable steGanOgraphy (HUGO), which achieves highly undetectable steganography by designing distortion functions that minimize differences between stego and cover images in SPAM second-order feature space. [PERSON] et al. [10] introduced Wavelet Obtained Weights (WOW), which uses directional filters to evaluate pixel modification impacts on high-frequency image information and defines a new distortion measurement method. [PERSON] et al. proposed the HILL algorithm [26], innovating on WOW by combining high-pass and low-pass filters to optimize distortion function design, fully utilizing carrier image texture features. Spatial Universal Wavelet Relative Distortion (S-UNIWARD) [11] achieves efficient information embedding in any domain by introducing the UNIWARD distortion function. This method uses wavelet transforms to extract high-frequency subband features, embedding information in hard-to-model regions, significantly enhancing steganographic analysis resistance and concealment.