We used publicly available activity recognition datasets - UCF101 and HMDB51. UCF101 - K. Soomro, A. R. Zamir, and M. Shah, “Ucf101: A dataset of 101 human actions classes from videos in the wild,” arXiv preprint arXiv:1212.0402, 2012. HMDB51 - H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “Hmdb: a large video database for human motion recognition,” in Proceedings of the International Conference on Computer Vision (ICCV). IEEE, 2011, pp. 2556–2563.

Cloud-based services have revolutionized data storage and processing tasks. However, these services raise security concerns as service providers may misuse the user’s stored data. Privacy loss is particularly problematic for hearing and speech impaired individuals that may need to use cloud infrastructure for sign language recognition (SLR). Addressing these challenges, this paper presents a privacy-preserving sign language recognition (P2SLR) as a cloud-service that operates over cloud infrastructure without revealing the individual’s visual information to the cloud service provider (CSP). The proposed P2SLR system is realized through two innovations: (a) block-based probabilistic image encryption scheme that combines fractional-order chaotic system (FOCS) and singular value decomposition (SVD) to obfuscate the visual information in video frames, and (b) a cloud-residing deep convolutional neural network (DCNN) based recognition architecture with a modified classifier to recognize gestures from encrypted video. The proposed scheme is validated for American, Argentinian, and German sign languages. The proposed scheme achieved recognition accuracy in the range 90:76 - 98:09%, comparable to existing state-of-the-art SLR techniques in the plain domain (PD). The proposed image encryption scheme is secure under standard cryptographic image attacks, protecting the individual’s identity. P2SLR is the first move towards developing a secure SLR system over the cloud.

The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images' visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes.

Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack.