# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 1 Introduction  
With the proliferation of social networking platforms, privacy protection has become an urgent challenge. Image steganography, as an information-hiding technique, achieves covert transmission by embedding sensitive information into multimedia content (such as images and videos). Unlike traditional encryption techniques, steganography utilizes the redundant space in digital media to embed information while avoiding third-party attention. This technology plays a crucial role not only in secure data transmission [1] but also finds widespread application in digital copyright identification and privacy protection, making it a key tool in information security [2]. Traditional image steganography techniques can be categorized into spatial domain and transform domain methods. Spatial domain-based methods, such as Least Significant Bit (LSB) replacement [3] and Pixel Value Differencing (PVD) [4], conceal information by directly modifying image pixel values, which are simple to implement but vulnerable to statistical analysis detection. Transform domain-based methods include Discrete Fourier Transform (DFT) [5], Discrete Cosine Transform (DCT) [6], and Discrete Wavelet Transform (DWT) [7] algorithms, which embed information by mapping images to the transformdomain, demonstrating superior detection resistance and robustness compared to spatial domain methods. The aforementioned methods are non-adaptive steganographic algorithms that overlook the impact of image texture complexity. Adaptive steganographic algorithms embed information in areas with complex textures and rich features, including Syndrome-Trellis Codes (STC) [8], which are linear code-based embedding techniques generating efficient and secure changes; Highly Undetectable steGanOgraphy (HUGO) [9], which uses prediction-error expansion to embed information in a hard-to-detect way; Wavelet Obtained Weights (WOW) [10] uses wavelet transform to obtain weights for embedding information, balancing embedding capacity and security; Spatial Universal Wavelet Relative Distortion (S-UNIWARD) [11] is a wavelet-domain distortion function measuring cover-stego relative distortion to reduce embedded information detectability. However, despite their advantages, traditional steganographic techniques face inherent limitations. Most methods rely on hand-crafted heuristics to determine embedding locations, which can lead to suboptimal performance when dealing with complex textures or adversarial detection models. Furthermore, as steganalysis techniques advance, traditional approaches struggle to maintain their security guarantees. To address these challenges, researchers have turned to deep learning-based methods, which enable adaptive and more robust information embedding strategies. Deep learning-based approaches have demonstrated significant potential in overcoming these limitations by automatically learning optimal embedding locations and feature representations. With the rapid development of deep learning technology, breakthrough progress has been made in image steganography. [PERSON] [12] proposed an end-to-end training steganographic network architecture, achieving effective information hiding in natural images. HiDDeN [13] can embed up to 0.2 bpp of information through training encoder and decoder networks. [PERSON] et al. [14] performed image embedding based on the U-Net [15] architecture, using convolutional neural networks to extract hidden information, though its generalization ability and robustness still require further validation. The emergence of Generative Adversarial Networks (GAN) [16] has further advanced steganographic technology. SteganoGAN [17] employed different encoder-decoder structures with a critic network but achieved decoding error rates of 5-30%. CHAT-GAN [18] enhanced steganographic image quality and information extraction accuracy through channel attention mechanisms, utilized stegananalysis networks as discriminators to improve security, and increased information recovery precision through error correction codes.