| Title | IoT Confidentiality: Steganalysis breaking point for J-UNIWARD using CNN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Mohamed, Nour, Rabie, Tamer, Kamel, Ibrahim |
| Conference Name | 2020 Advances in Science and Engineering Technology International Conferences (ASET) |
| Keywords | CNN, composability, convolutional neural networks, Correlation, Databases, DCT, jpeg, Metrics, privacy, pubcrawl, smart cities, steganalysis, steganography, steganography detection, Transform coding, wavelet transforms |
| Abstract | The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%. |
| DOI | 10.1109/ASET48392.2020.9118272 |
| Citation Key | mohamed_iot_2020 |
IoT Confidentiality: Steganalysis breaking point for J-UNIWARD using CNN