# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 4 Results and Discussion
### Authentication Analysis  
Visual-IoT communication channels are usually insecure, and when image data are transmitted from one node to another there are chances for it being tampered with, as a result of various intentional and unintentional attacks. For a reversible data-hiding technique, extraction of the original image and secret data are the prime requirements. An erroneous extraction as a result of an intentional /un-intentional attack leads to unfaithful retrieval. For authentication of both the images and the secret data, a fragile watermark is embedded into the cover image. This watermark is first extracted from the stego-image at the receiving end, and then compared to the embedded one, to facilitate authentication. If the two watermarks, embedded and extracted, are the same, the reception is considered error-free. Since, in this technique, we have interleaved watermark bits throughout the whole cover image (one bit after every 47 data bits), tampering with any part of the image is reflected at the output. Various signal processing and geometric attacks were used to test the proposed scheme. It is pertinent to mention here that all the stego images (Image-1 through Image-12) were tested with both techniques, using the aforementioned attacks. However, due to space limitations, we have arbitrarily selected some of the images, as presented in Figures 7 and 8. From Figures 7 and 8, it is evident that any tampering with the stego-image results in extraction of the unrecognized watermark, hence validating the fact that the scheme is sensitive to various signal processing and geometrical attacks carried out on it. The average BER in the proposed scheme, to various attacks carried out on stego images, was 43.91% and 45.14% for the two techniques, respectively, which ensures that any attack on the data during transit would be successfully detected. In addition to the subjective analysis, we also carried out an objective analysis of the two schemes, presented for various attacks, such as salt and pepper noise addition and median filtering, for the whole set of images. The objective quality metrics obtained by subjecting the stego-images to different attacks are presented in Table 5. For an attacked image, the SSIM yields a very low value, while the BER results in a higher value. The higher BER values and lower SSIM values, along with the PSNR, show that the scheme is highly fragile, and hence capable of detecting any tamper. Tables 5 and 6 present the exact count of errors for a payload of 1.5 bpp, along with various objective quality metrics for technique-1 and -2, respectively. their technique was not able to detect tamper, a significant parameter of importance in critical applications, such as healthcare, driven by visual-IoT. We fully demonstrated the reversibility of the proposed scheme in Section 4.2. In addition to authenticating the data received, we tested our system for various signal processing and geometric attacks, such as Gaussian noise, salt and pepper noise, median filtering, low pass filtering, sharpening, JPEG compression, etc. The image quality parameters PSNR, SSIM, and BER were for evaluation. It was shown in detail in Section 4.3 that all attacks carried out on the stego-images reported a BER of more than 50%, signifying that the hidden fragile watermark was not faithfully received. This informs the receiver that the data has been attacked during transit. An IoT-based system is resource-constrained, and thus has lesser memory/bandwidth available. This demands that the processes carried out by these systems be less computationally complex. Towards this end, the significant contribution of this paper is the development and use of ODNMI. The use of ODNMI increases the computational efficiency of the system, as it requires only 50% of the operation and storage resources of well-known schemes, such as [21; 22; 23; 25; 27], while maintaining reversibility and a higher payload.