Dual Image Based Reversible Data Hiding Scheme Using Three Pixel Value Difference Expansion

Giri, Debasis; Jana, Biswapati; Kumar, Mondal Shyamal

doi:10.1007/978-81-322-2752-6_40

Cited by 4 publications

(2 citation statements)

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“…Debasis et al. proposed a dual‐image‐based reversible data hiding scheme utilizing three pixel value differences and difference expansion [12]. Meikap et al.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these methods utilized different techniques such as weighted matrices, hamming codes, image interpolation etc., to achieve the goal of hiding data within images and fully recovering the original image when necessary. Debasis et al proposed a dual-imagebased reversible data hiding scheme utilizing three pixel value differences and difference expansion [12]. Meikap et al utilized generalized the directional pixel value overlapping (DPVO) technique for reversible data hiding [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hiding image into image with hybrid attention mechanism based on GANs

Zhu,

Dong,

Song

et al. 2024

IET Image Processing

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Image steganography is the art of concealing secret information within images to prevent detection. In deep‐learning‐based image steganography, a common practice is to fuse the secret image with the cover image to directly generate the stego image. However, not all features are equally critical for data hiding, and some insignificant ones may lead to the appearance of residual artifacts in the stego image. In this article, a novel network architecture for image steganography with hybrid attention mechanism based on generative adversarial network is introduced. This model consists of three subnetworks: a generator for generate stego images, an extractor for extracting the secret images, and a discriminator to simulate the detection process, which aids the generator in producing more realistic stego images. A specific hybrid attention mechanism (HAM) module is designed that effectively fuses information across channel and spatial domains, facilitating adaptive feature refinement within deep image representations. The experimental results suggest that the HAM module not only enhances the image quality during both the steganography and extraction processes but also improves the model's undetectability. Stego images are mixed with varying levels of noise in the training process, which can further improve robustness. Finally, it is verified that the model outperforms current steganography approaches on three datasets and exhibits good undetectability.

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“…Debasis et al. proposed a dual‐image‐based reversible data hiding scheme utilizing three pixel value differences and difference expansion [12]. Meikap et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%