191 research outputs found
Improving Robustness of TCM-based Robust Steganography with Variable Robustness
Recent study has found out that after multiple times of recompression, the
DCT coefficients of JPEG image can form an embedding domain that is robust to
recompression, which is called transport channel matching (TCM) method. Because
the cost function of the adaptive steganography does not consider the impact of
modification on the robustness, the modified DCT coefficients of the stego
image after TCM will change after recompression. To reduce the number of
changed coefficients after recompression, this paper proposes a robust
steganography algorithm which dynamically updates the robustness cost of every
DCT coefficient. The robustness cost proposed is calculated by testing whether
the modified DCT coefficient can resist recompression in every step of STC
embedding process. By adding robustness cost to the distortion cost and using
the framework of STC embedding algorithm to embed the message, the stego images
have good performance both in robustness and security. The experimental results
show that the proposed algorithm can significantly enhance the robustness of
stego images, and the embedded messages could be extracted correctly at almost
all cases when recompressing with a lower quality factor and recompression
process is known to the user of proposed algorithm.Comment: 15 pages, 5 figures, submitted to IWDW 2020: 19th International
Workshop on Digital-forensics and Watermarkin
Hide Secret Information in Blocks: Minimum Distortion Embedding
In this paper, a new steganographic method is presented that provides minimum
distortion in the stego image. The proposed encoding algorithm focuses on DCT
rounding error and optimizes that in a way to reduce distortion in the stego
image, and the proposed algorithm produces less distortion than existing
methods (e.g., F5 algorithm). The proposed method is based on DCT rounding
error which helps to lower distortion and higher embedding capacity.Comment: This paper is accepted for publication in IEEE SPIN 2020 conferenc
Information Forensics and Security: A quarter-century-long journey
Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques
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