Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge

05/09/2021
by   Weixuan Tang, et al.
11

Although significant progress in automatic learning of steganographic cost has been achieved recently, existing methods designed for spatial images are not well applicable to JPEG images which are more common media in daily life. The difficulties of migration mostly lie in the unique and complicated JPEG characteristics caused by 8x8 DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. The policy network is constructed following a domain-transition design paradigm, where three modules including pixel-level texture complexity evaluation, DCT feature extraction, and mode-wise rearrangement, are proposed. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with 8x8 DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature based and modern CNN based steganalyzers.

READ FULL TEXT

Authors

page 3

page 4

page 5

page 6

page 8

page 9

page 10

page 11

08/06/2019

An Efficient JPEG Steganographic Scheme Design Using Domain Transformation of Embedding Cost

Although the recently proposed JPEG steganography using Block embedding ...
02/14/2022

Sequential Bayesian experimental designs via reinforcement learning

Bayesian experimental design (BED) has been used as a method for conduct...
11/24/2021

Universal Deep Network for Steganalysis of Color Image based on Channel Representation

Up to now, most existing steganalytic methods are designed for grayscale...
01/09/2020

Population-Guided Parallel Policy Search for Reinforcement Learning

In this paper, a new population-guided parallel learning scheme is propo...
03/24/2018

CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography

Historically, steganographic schemes were designed in a way to preserve ...
06/22/2020

Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving

In the past decades, we have witnessed significant progress in the domai...
07/13/2020

DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space

It has been arduous to assess the progress of a policy learning algorith...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.