BITS Faculty Publications
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Item Example Based Privacy-Preserving Video Color Grading(Springer, 2019) Rajput, Amitesh SinghThe integration of cloud computing and smart multimedia gadgets has made an attractive business model today. However, data privacy is one of the major concern when moving to third party driven infrastructures like cloud. Furthermore, due to diverse camera sensors, the captured multimedia may contain insufficient lightning/colors and processing them manually is a painstakingly task. A few schemes have been proposed to address this problem, however they suffered from the major drawback of computational and storage overhead, and becomes non-trivial in case of videos. Considering these challenges, we propose an automatic video color grading approach in this chapter. The proposed approach enables cloud data center to process encrypted multimedia data by transferring its colors as per an example image as the reference. We analyze the correlation between consecutive video sequences and propose to evaluate the color transformation parameters for every alternate video frame. In addition, proxy encryption based Paillier cryptosystem has been used for video encryption. As a result, the computational and storage overheads are drastically reduced with effective video grading results. The feasibility and robustness of the proposed approach are validated through various tests.Item Privacy-preserving smart surveillance using local color correction and optimized ElGamal cryptosystem over cloud(IEEE, 2019) Rajput, Amitesh SinghThe emergence of cloud computing in integration with smart multimedia devices has created an attractive business model today. However, due to the involvement of third party servers, there is a risk of privacy for highly confidential data like surveillance images/videos. Moreover, due to inconsistent lightning conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. Addressing these problems, we propose a novel cloud based privacy-preserving approach for image color enhancement in this paper. Unlike existing color correction schemes, where colors of the test image are processed in plain domain with visible image contents, we propose to perform color correction operations in the encrypted domain over cloud. As a consequence, superior results are achieved along with complete privacy assurance. In addition, we propose a block-based image encryption method using logistic-tent system and ElGamal cryptosystem. As a result, size of the encrypted image is significantly reduced as compared to the naive approach. Experimental results are performed under various tests and the proposed approach is found to be highly effective as compared to state-of-the-art schemes. Moving ahead, security strength of the proposed approach is demonstrated through a challenge response game model.Item A privacy-preserving protocol for efficient nighttime haze removal using cloud based automatic reference image selection and color transfer as a service(Elsevier, 2020-01) Rajput, Amitesh SinghThe advanced Internet technologies have migrated the people to rejoice a virtual environment known as cloud computing. The user can avail the desired services on a pay-as-you-go model, without worrying about the burden of infrastructure maintenance. However, privacy is one of the major issues in cloud computing. This issue is further widened for highly confidential multimedia data like surveillance images and videos. In the context of cloud based smart multimedia systems, it has been found that due to inconsistent weather conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. However, privacy related concerns are resisting users to move their data to the cloud. One such problem is addressed in this paper, specializing the task of efficient nighttime haze removal using privacy-preserving cloud based automatic reference image selection and color transfer as a service. Different from daytime conditions, nighttime haze image consists of multiple light sources, which makes an ambiguous situation for haze removal. We address this problem by first selecting an appropriate gray image as the reference and then transferring its colors to nighttime haze image. This makes the transformed image a suitable candidate for radiance recovery. The proposed protocol is designed to securely outsource this considerable burden from user end. We accomplish this by first proposing an automatic reference gray image selection method, followed by efficient handling mechanisms for technical challenges arising due to performing color transfer operations securely over cloud. Experimental results and validation demonstrates superiority of the proposed method over state-of-the-art schemes. Security analysis of the proposed protocol is established through a challenge-response game model.Item Privacy-preserving human action recognition as a remote cloud service using RGB-D sensors and deep CNN(Elsevier, 2020-08) Rajput, Amitesh SinghCloud-based expert systems are highly emerging nowadays. However, the data owners and cloud service providers are not in the same trusted domain in practice. For the sake of data privacy, sensitive data usually has to be encrypted before outsourcing which makes effective cloud utilization a challenging task. Taking this concern into account, we propose a novel cloud-based approach to securely recognize human activities. A few schemes exist in the literature for secure recognition. However, they suffer from the problem of constrained data and are vulnerable to re-identification attack, where advanced deep learning models are used to predict an object’s identity. We address these problems by considering color and depth data, and securing them using position based superpixel transformation. The proposed transformation is designed by actively involving additional noise while resizing the underlying image. Due to this, a higher degree of obfuscation is achieved. Further, in spite of securing the complete video, we secure only four images, that is, one motion history image and three depth motion maps which are highly saving the data overhead. The recognition is performed using a four stream deep Convolutional Neural Network (CNN), where each stream is based on pre-trained MobileNet architecture. Experimental results show that the proposed approach is the best suitable candidate in “security-recognition accuracy (%)” trade-off relation among other image obfuscation as well as state-of-the-art schemes. Moreover, a number of security tests and analyses demonstrate robustness of the proposed approach.Item Securing Multimedia Videos Using Space-Filling Curves(Springer, 2022-04) Rajput, Amitesh SinghSecuring online multimedia content has become a significant concern in this digital era. Nowadays, several organizations provide premium video content for skill development, academics and entertainment. The usage of Space-Filling Curves (SFC) for video and image encryption was initiated in the late 20th century. Although it is a promising approach for enforcing multimedia security, a cryptanalysis on SFCs was performed, which rendered the technique useless. In this paper, we have presented two strategies for countering the chosen plaintext-based attack. In the first method, the notion of diffusion is introduced in video frames by swapping two colour channels. Alternatively, in the second method, the frame sequences are scrambled twice to overcome the constraint of their restricted movements. Finally, the output pixel intensity values are modified to model a uniform distribution. Our empirical results demonstrate the superiority of the presented work over state-of-the-art approaches involving multimedia security. We have also evaluated the security of our approaches through standard measures like entropy, histogram analysis, and differential analysis. Hence, the presented work provides a holistic framework for securely distributing multimedia content over both online and offline platforms.