Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions
Steganography and steganalysis are two interrelated aspects of the field of information
security. Steganography seeks to conceal communications, whereas steganalysis aims to …
security. Steganography seeks to conceal communications, whereas steganalysis aims to …
Deep learning for diverse data types steganalysis: A review
Steganography and steganalysis are two interrelated aspects of the field of information
security. Steganography seeks to conceal communications, whereas steganalysis is aimed …
security. Steganography seeks to conceal communications, whereas steganalysis is aimed …
A new adversarial embedding method for enhancing image steganography
Image steganography aims to embed secret messages into cover images in an
imperceptible manner. While steganalysis tries to identify stegos from covers, which is a …
imperceptible manner. While steganalysis tries to identify stegos from covers, which is a …
Steganography using a 3-player game
Image steganography aims to securely embed secret information into cover images. Until
now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, were among the …
now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, were among the …
Dataset mismatched steganalysis using subdomain adaptation with guiding feature
The generalization problem in deep learning has always been an important problem to be
solved. In the field of steganalysis, generalization is also an important factor that makes …
solved. In the field of steganalysis, generalization is also an important factor that makes …
Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges
Malware distribution to the victim network is commonly performed through file attachments in
phishing email or from the internet, when the victim interacts with the source of infection. To …
phishing email or from the internet, when the victim interacts with the source of infection. To …
A generative learning steganalysis network against the problem of training-images-shortage
H Zhang, Z Song, Q Xing, B Feng, X Lin - Electronics, 2022 - mdpi.com
In recent years, several steganalysis neural networks have been proposed and achieved
satisfactory performances. However, these deep learning methods all encounter the …
satisfactory performances. However, these deep learning methods all encounter the …
Novel hidden bit location method towards JPEG steganography
B Pan, T Qiao, J Li, Y Chen… - Security and …, 2022 - Wiley Online Library
Image steganalysis has been widely studied, most of which can only complete the binary
task of identifying the existence of hidden bits in an inquiry image. Currently, although some …
task of identifying the existence of hidden bits in an inquiry image. Currently, although some …
Distribution-preserving-based automatic data augmentation for deep image steganalysis
In recent years, deep learning-based steganalyzers far outperformed handcrafted feature-
based steganalyzers. However, a large amount of data is needed to train deep learning …
based steganalyzers. However, a large amount of data is needed to train deep learning …
Analysis of the scalability of a deep-learning network for steganography “into the wild”
Since the emergence of deep learning and its adoption in steganalysis fields, most of the
reference articles kept using small to medium size CNN, and learn them on relatively small …
reference articles kept using small to medium size CNN, and learn them on relatively small …