# Bans--Learning Steganography with a Binary Attention Mechanism
## 1 Introduction  
Image steganography aims at delivering a modified cover image to secretly transfer hidden information inside with little awareness of the third-party supervision. On the other side, steganalysis algorithms are developed to find out whether an image is embedded with hidden information or not, and, therefore, resisting steganalysis detection is one of the major indicators of steganography security. In the meantime, with the booming trend of convolutional neural networks, a massive amount of neural-network-automated tasks are coming into industrial practices like image auto-labeling through object detection [1; 2] and classification [3; 4], face recognition [5], pedestrian re-identification [6], etc. Image steganography is now facing a more significant challenge from these automated tasks, whose embedding distortion might influence the task result in a great manner and irresistibly lead to suspicion. Figure 1 is an example that LSB (Least Significant Bit)-Matching [7] steganography completely alters the image classification result from goldfish to proboscis monkeys. Under such circumstances, a steganography model even with outstanding invisibility to steganalysis methods still cannot be called secure where the spurious label might re-arouse suspicion and, finally, all efforts are made in vain (Source code will be published at: [[https://github.com/adamcavendish/BASN-Learning-Steganography-with-Binary-Attention-Mechanism](https://github.com/adamcavendish/BASN-Learning-Steganography-with-Binary-Attention-Mechanism)]([https://github.com/adamcavendish/BASN-Learning-Steganography-with-Binary-Attention-Mechanism](https://github.com/adamcavendish/BASN-Learning-Steganography-with-Binary-Attention-Mechanism))). The cover image and embedded image both use an ImageNet pretrained ResNet-18 [3] network for classification. The percentage before the predicted class label represents a network's confidence in prediction. The red, green, and blue noisy images in the center represent the altered pixel locations in corresponding channels during steganography. There are only three kinds of colors within these images where white stands are for no modification, the lighter one stands for a +1 modification, and the darker one stands for a \(-1\) modification. Most previous steganography models focus on resisting steganalysis algorithms or raising embedding payload capacity. BPCS (Bit-Plane Complexity Segmentation) [8; 9] and PVD (Pixel-Value Differencing) [10; 11; 12] use adaptive embedding based on local complexity to improve embedding visual quality. HuGO [13] and S-UNIWARD [14] resist steganalysis by minimizing a suitably defined distortion function. [PERSON] [15] adopts a gray level co-occurence matrix to calculate the image texture complexity. [PERSON]'s method [16] improves security and robustness with the aid of Faster R-CNN's object detection on texture complex areas. [PERSON] [17] proposes optimal dispersion degree on halftone images to measure region texture complexity. [PERSON] [18] adds a texture-based loss to help GAN hide information. [PERSON] [19] establishes a framework with image texture complexity and distortion distribution strategies to embed hidden information across multiple cover images. [PERSON] [20] adopts a deep convolutional generative adversarial network to achieve steganography without embedding. [PERSON] [21] and [PERSON] [22] achieve a vast payload capacity by focusing on image-into-image steganography. In this paper, we propose a Binary Attention Steganography Network (abbreviated as **BASN**) architecture to achieve a relatively high payload capacity (2-3 bpp, bits per pixel) with minimal distortion to other neural-network-automated tasks. It utilizes convolutional neural networks with two attention mechanisms, which minimize embedding distortion to the human visual system and neural network feature maps, respectively.