{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-trained-cnn-encode-decoder","title":"End-to-end Trained CNN Encode-Decoder Networks for Image Steganography","arxiv_id":"1711.07201","date":"2017-11-20","proceeding":null,"authors":["Atique ur Rehman","Rafia Rahim","M Shahroz Nadeem","Sibt Ul Hussain"],"abstract":"All the existing image steganography methods use manually crafted features to\nhide binary payloads into cover images. This leads to small payload capacity\nand image distortion. Here we propose a convolutional neural network based\nencoder-decoder architecture for embedding of images as payload. To this end,\nwe make following three major contributions: (i) we propose a deep learning\nbased generic encoder-decoder architecture for image steganography; (ii) we\nintroduce a new loss function that ensures joint end-to-end training of\nencoder-decoder networks; (iii) we perform extensive empirical evaluation of\nproposed architecture on a range of challenging publicly available datasets\n(MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art\npayload capacity at high PSNR and SSIM values.","url_abs":"http://arxiv.org/abs/1711.07201v1","url_pdf":"http://arxiv.org/pdf/1711.07201v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"end-to-end-trained-cnn-encode-decoder","repo_url":"https://github.com/marcovaldong/isgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"end-to-end-trained-cnn-encode-decoder","repo_url":"https://github.com/qzramiz/End-To-End-Image-Steganography","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"end-to-end-trained-cnn-encode-decoder","repo_url":"https://github.com/saadzia10/Steganography-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"end-to-end-trained-cnn-encode-decoder","repo_url":"https://github.com/junhyeog/EDS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-steganography","task_name":"Image Steganography"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.07201","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}