TY - JOUR
T1 - ASSAF
T2 - Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks
AU - Cohen, Assaf
AU - Cohen, Aviad
AU - Nissim, Nir
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG's quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%.
AB - Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG's quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%.
KW - Autoencoder
KW - Convolution neural network
KW - Deep learning
KW - Siamese neural network
KW - Steganalysis
KW - Steganography
UR - http://www.scopus.com/inward/record.url?scp=85088893389&partnerID=8YFLogxK
U2 - 10.1016/j.neunet.2020.07.022
DO - 10.1016/j.neunet.2020.07.022
M3 - Article
C2 - 32759032
AN - SCOPUS:85088893389
SN - 0893-6080
VL - 131
SP - 64
EP - 77
JO - Neural Networks
JF - Neural Networks
ER -