Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 31, Issue 7, Pages 2779-2791Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2020.3033945
Keywords
Coverless steganography; object detection; multi-object recognition; faster RCNN; ResNet
Categories
Funding
- National Natural Science Foundation of China [61772561, 62002392]
- Key Research and Development Plan of Hunan Province [2019SK2022]
- Science Research Projects of Hunan Provincial Education Department [18A174]
- Degree and Post-Graduate Education Reform Project of Hunan Province [2019JGYB154]
- Postgraduate Education and Teaching Reform Project of Central South University of Forestry and Technology [2019JG013]
- Natural Science Foundation of Hunan Province [2020JJ4141, 2020JJ4140]
- Postgraduate Excellent Teaching Team Project of Hunan Province [[2019]370-133]
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The proposed method utilizes multi-object recognition to generate robust binary sequences, introduces a novel mapping rule, effectively resists geometric attacks and noise attacks, with images remaining unmodified during transmission, demonstrating strong robustness.
Most of the existing coverless steganography approaches have poor robustness to geometric attacks, because these approaches use features of the entire image to map information, and these features are easy to be lost when being attacked. In order to improve the robustness against geometric attacks, we propose a coverless image steganography method based on multi-object recognition. In this scheme, we firstly use Faster RCNN to detect objects in the image data set, establish a mapping dictionary between object labels and binary sequence. Then we propose a novel mapping rule based on the filtered robust object labels for sequence generation. Therefore, an image can generate robust binary sequence through multiobjects recognition. In the transmission process, the transmitted image has not been modified, so our method can fundamentally resist steganalysis tools and avoid the attacker's suspicions. In addition, the capacity and hiding rate of the proposed method are both considerable. Evaluations with under geometric attacks shows, on average, 3.1x robustness increase over other five coverless steganography methods. Moreover, evaluations under ten noise attacks shows, on average, the robustness of our method is also excellent, which reaches 83%.
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