# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 2 Related Literature  
IoT-based services have become an indispensable part of smart-city services. They have applications in healthcare, maintenance, autonomous vehicles, etc. Visual-IoT infrastructure requires large bandwidth, resulting in a challenging gap between computation and communication. Moreover, the data exchanged is highly vulnerable to attacks. To ensure better security, and lesser bandwidth requirements, data hiding could be used. Some of the data hiding works that could be used, in visual-IoT in general and smart-health in particular, are presented as follows. Conventional data-hiding techniques, such as Least Significant Bit (LSB) substitution [14], LSB matching [15], and Pixel Value Differences (PVD), result in cover media distortion, which remains even after the hidden data are extracted. Though these irreversible data-hiding techniques provide improved data-embedding capabilities, they cannot be used in applications were extraction of both the secret data and the original image is the prime requirement. Examples of such cases include military applications, digital archiving, medical diagnosis, and law enforcement, wherein it is critical to reverse the marked media back to the original cover media after retrieving the hidden data. Reversible Data Hiding (RDH) is used to ensure perfect retrieval of the cover image from stego-image, and as such is used for critical applications, such as healthcare. 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. Nature-inspired computing-based RDH schemes, based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been reported in [22; 23]. These schemes are computationally complex, and handle less payload. Low embedding capacity techniques are not feasible in bandlimited environments. This is because of the fact that the secret data would require a large number of cover images for secure communication, increasing bandlimited channel load and reducing throughput. Maintaining reversibility and ensuring high payload is of paramount importance in a visual-IoT-driven smart-healthcare system. Interpolation-based RDH schemes have been found to have a high embedding capacity. The majority of interpolation-based RDH schemes firstly downscale a 2N \(\times\) 2M image to an N \(\times\) M image, which is interpolated to the size of 2N \(\times\) 2M. Some prominent examples in this direction include Interpolation by Neighboring Pixels (INP) [24], Neighborhood Mean Interpolation (NMI) [25], the Pixel Repetition Method (PRM), the Pixel Permutation Method (PPM) [26], and Optimal Pixel Repetition (OPR) [27]. The first interpolation-based RDH scheme using NMI was proposed by [PERSON] and [PERSON] [25]. This scheme is capable of hiding data reversibly, as well as irreversibly, and provides a PSNR of above 35 dB for a payload of 0.96 bpp. It was later modified by [PERSON] et al. [28] using histogram modification, with an increase in payload. Marginal improvements to NMI [25] were seen in [29]. [PERSON] and [PERSON] [24] proposed another method, called Interpolation using Neighboring Pixels (INP), with a higher data-embedding capacity. In INP and NMI, simple operations are executed on the seed pixels, such as calculating the mean of the two neighboring pixels so that newly created pixels bear a resemblance to their neighboring pixels. PRM repeats the seed pixel, and hence results in a 2 \(\times\) 2 block from one seed pixel, while in PPM, some basic mathematical operations are used to create new pixels, which are permuted pixels for data embedding as per [26]. In OPR [27], the seed pixels are used to obtain the optimal values of embedding pixels.