A Robust Coverless Steganography Scheme Using Camouflage Image

Liu, Qiang; Xiang, Xuyu; Qin, Jiaohua; Tan, Yun; Zhang, Qin

doi:10.1109/tcsvt.2021.3108772

Cited by 27 publications

(6 citation statements)

References 44 publications

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“…Most of the steganography methods for uncovered images are based on robust mapping rules. Most steganography methods of this class are based on robust mapping rules [19][20][21]. There are other methods to map the message to the object in the image and extract the message using the target recognition method [22].However, the payload of this method is significantly lower than that of the other two methods.…”

Section: Three Kinds Of Steganography Methodsmentioning

confidence: 99%

Communication Scheme for Steganography based on Image Reconstruction Technology with Information-Pixels Conversion

Zhao,

Wang,

Qin

et al. 2023

Preprint

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Digital image steganography is a technique for hiding information in an image with the aim of hiding the presence of the information itself. A new steganography without embedding scheme is proposed in this paper to create stego images based only on secret information, in order to avoid image distortion caused by pixel modification-based embedding methods. In this scheme, according to the proposed interconversion algorithm between messages and pixels, the secret message is encoded into pixel format and placed in a blank image in a specified way, which is considered a broken image to be restored. The broken image is repaired by the training network to obtain the stego image. The receiver selects specific stego pixels from the received image and restores them to the delivered secret message according to the inverse of the transformation. In addition, the proposed communication framework supports the choice of different generative network structures, further improving the resistance to steganalysis. The basic generation model was trained and tested on the collected cat face images and bedroom images from the LSUN dataset, respectively, and the steganographic images were evaluated qualitatively and quantitatively. The final experimental results verify that the framework achieves satisfactory performance in steganography capacity and information recovery rate compared with other methods without embedding.

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Section: Three Kinds Of Steganography Methodsmentioning

confidence: 99%

Communication Scheme for Steganography based on Image Reconstruction Technology with Information-Pixels Conversion

Zhao,

Wang,

Qin

et al. 2023

Preprint

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“…To enhance resilience against geometric attacks, Luo et al [22] introduced a faster regionbased convolutional neural network (faster-RCNN). In the context of system security, Liu et al [23] proposed an improvement by transmitting disguised images instead of steganographic images to the receiver. Although generativebased coverless steganography developed a lot, some common defects exist in it as follows.…”

Section: Introductionmentioning

confidence: 99%

A Robust Coverless Image Steganography Algorithm Based on Image Retrieval with SURF Features

Li,

Liu,

Dong

et al. 2024

Security and Communication Networks

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With the advancement of image steganography, coverless image steganography has gained widespread attention due to its ability to hide information without modifying the carrier of images. However, existing coverless image steganography methods often require both communicating parties to transmit an amount of additional information including image blocks’ locations or a large number of parameters, which will raise a serious suspicion. In light of this issue, we propose a robust coverless image steganography algorithm based on Speeded-Up Robust Features (SURF). Firstly, the proposed method allows both communicating parties to independently create multiple coverless image datasets (CIDs) using random seeds. Then, a mapping rule is designed for creating one-to-one correspondence between hash sequences and images in CIDs. Finally, the secret information will be carried by the images whose hash sequences are equal to the secret segments. At the receiver side, the robust SURF of images is utilized to retrieve the secret information. Experimental results demonstrate that the proposed algorithm outperforms other methods in terms of capacity, robustness, and security. Furthermore, it is worth noting that the proposed method eliminates the need to transmit a large amount of additional information, which is a significant security issue in existing coverless image steganography algorithms.

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“…Although these methods are more resistant to typical noise attacks than spatial domain-based ones, they are more vulnerable to geometric attacks. Luo et al [20], therefore, applied deep learning in coverless steganography by utilizing Faster Region-Based Convolutional Neural Networks (Faster Method Advantages Disadvantages [13], [14] Simple functional implementation Small hiding capacity and poor robustness [15] Stronger robustness against rotation & scaling attacks, improved hiding capacity High computational complexity [17] Further increased capacity Limited by molecular structure images of material (MSIM) [18] More robust against noise attacks Fragile to geometric attacks [19] Ability to handle abrupt signals better than the DCT used in [18] Fragile to geometric attacks [20][21][22][23] More robust against geometric attacks Less robust against noise attacks compared to frequency domain-based methods [24,25] Less database load Small hiding capacity, unnatural image generation, low detection accuracy [26] Increased security with styles transfer Low extraction accuracy [7] Higher security and robustness Small hiding capacity Ours Low collision rate of the feature sequences and the minimal burden on the database A slightly lower robustness Tab. 1.…”

Section: Introductionmentioning

confidence: 99%

“…This multi-object identification produces robust binary sequences that increase robustness against geometric attacks. Other deep learningbased coverless steganography exists as well, like [21][22][23]. These techniques are robust to geometric attacks but less tolerant to noise.…”

Section: Introductionmentioning

confidence: 99%

Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain

Tan,

Liu,

Zhou

et al. 2023

RADIOENGINEERING

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Coverless image steganography typically extracts feature sequences from cover images to map information. Once the extracted features have high similarity, it is challenging to construct a complete mapping sequence set, which places a heavy burden on the underlying storage and computation. In order to improve database utilization while increasing the data-hiding capacity, we propose a coverless steganography model based on low-similarity feature selection in the DCT domain. A mapping algorithm is presented based on an 8000-dimensional feature termed CS-DCTR extracted from each image to convert into binary sequences. The high feature dimension leads to a high capacity, ranging from 8 to 25 bits per image. Furthermore, scrambling is employed for feature mapping before building an inverted index tree, considerably enhancing security against steganalysis. Experimental results show that CS-DCTR features exhibit high diversity, averaging 49.3% complete mapping sequences, which indicates lower similarity among CS-DCTR features. The technique also demonstrates resistance to normal operations and benign attacks. The information extraction accuracy rises to 96.7% on average under typical noise attacks. Moreover, our technique achieves excellent performance in terms of hiding capacity, image utilization, and transmission security.

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A Robust Coverless Steganography Scheme Using Camouflage Image

Cited by 27 publications

References 44 publications

Communication Scheme for Steganography based on Image Reconstruction Technology with Information-Pixels Conversion

Communication Scheme for Steganography based on Image Reconstruction Technology with Information-Pixels Conversion

A Robust Coverless Image Steganography Algorithm Based on Image Retrieval with SURF Features

Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain

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