Realization of a Reversible Data Hiding Framework using Histogram Bin Shifting for Color Images

Hussan, Muzamil; Parah, Shabir A.; Qurashi, Ghulam.J.

doi:10.1109/iciip47207.2019.8985824

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…RDH is being studied with vigor by the research community, to attain the futuristic and sustainable goals of secure and reliable smart-healthcare setups. Difference Expansion (DE) [16], Prediction Error Expansion [17,18], and Histogram bin shifting (HBS) [19][20][21] have been used for developing RDH schemes. However, the schemes based on DE and HBS are characterized by low embedding capacity.…”

Section: Related Literaturementioning

confidence: 99%

SVIoT: A Secure Visual-IoT Framework for Smart Healthcare

Kaw

Gull

Parah

2022

Sensors

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The advancement of the Internet of Things (IoT) has transfigured the overlay of the physical world by superimposing digital information in various sectors, including smart cities, industry, healthcare, etc. Among the various shared information, visual data are an insensible part of smart cities, especially in healthcare. As a result, visual-IoT research is gathering momentum. In visual-IoT, visual sensors, such as cameras, collect critical multimedia information about industries, healthcare, shopping, autonomous vehicles, crowd management, etc. In healthcare, patient-related data are captured and then transmitted via insecure transmission lines. The security of this data are of paramount importance. Besides the fact that visual data requires a large bandwidth, the gap between communication and computation is an additional challenge for visual IoT system development. In this paper, we present SVIoT, a Secure Visual-IoT framework, which addresses the issues of both data security and resource constraints in IoT-based healthcare. This was achieved by proposing a novel reversible data hiding (RDH) scheme based on One Dimensional Neighborhood Mean Interpolation (ODNMI). The use of ODNMI reduces the computational complexity and storage/bandwidth requirements by 50 percent. We upscaled the original image from M × N to M ± 2N, dissimilar to conventional interpolation methods, wherein images are upscaled to 2M × 2N. We made use of an innovative mechanism, Left Data Shifting (LDS), before embedding data in the cover image. Before embedding the data, we encrypted it using an AES-128 encryption algorithm to offer additional security. The use of LDS ensures better perceptual quality at a relatively high payload. We achieved an average PSNR of 43 dB for a payload of 1.5 bpp (bits per pixel). In addition, we embedded a fragile watermark in the cover image to ensure authentication of the received content.

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