| Title | Multi-data Image Steganography using Generative Adversarial Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Sultan, Bisma, Wani, M. Arif |
| Conference Name | 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) |
| Date Published | mar |
| Keywords | Deep Learning, Deep learning steganography, distortion, Generative Adversarial Learning, generative adversarial networks, Generators, Metrics, Multi-secret data, PSNR, pubcrawl, Receivers, resilience, Resiliency, Scalability, steganography |
| Abstract | The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates. |
| DOI | 10.23919/INDIACom54597.2022.9763273 |
| Citation Key | sultan_multi-data_2022 |
Multi-data Image Steganography using Generative Adversarial Networks