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New issues have arisen in the creation of 3D objects linked to collaboration in 3D workflows. In this kind of usage it may be necessary to allow access and to share a lower quality file of a 3D object for some collaborators.
In this paper, we present a Format-Compliant Selective Secret 3D Object Sharing (FCSS3DOS) scheme, which visually protects the content using geometrical distortions by selectively sharing bits of a 3D object's geometry using Shamir's Secret Sharing scheme.

This paper proposes a novel framework to regularize the highly illposed and non-linear Fourier ptychography problem using generative models. We demonstrate experimentally that our proposed algorithm, Deep Ptych, outperforms the existing Fourier ptychography techniques, in terms of quality of reconstruction and robustness against noise, using far fewer samples. We further modify the proposed approach to allow the generative model to explore solutions outside the range, leading to improved performance.

This work presents a reduced complexity image clustering (RCIC) algorithm that blindly groups images based on their camera fingerprint. The algorithm does not need any prior information and can be implemented without and with attraction, to refine clusters. After a camera fingerprint is estimated for each image in the data set, a fingerprint is randomly selected as reference fingerprint and a cluster is constructed using this fingerprint as centroid. The clustered fingerprints are removed from the data set and the remaining fingerprints are clustered repeating the same process.

While the phylogenetic analysis of multimedia documents keeps being investigated, some recent studies have shown the possibility of re-using the same strategies to analyze the evolution of computer programs (Software Phylogeny), considering its several applications spanning from copyright enforcement to malware detection.

Despite significant advances in recent years, the problem of image stitching still lacks a robust solution. Most of the feature based image stitching algorithms perform image alignment based on either homography-based transformation or content-preserving warping. Pairwise homography-based approach miserably fails to handle parallax whereas content-preserving warping approach does not preserve the structural property of the images. In this paper, we propose a nonlinear optimization to find out the global homographies using pairwise homography estimates and point correspondences.

Nowadays, speech spoofing is so common that it presents a great challenge to social security. Thus, it is of great significance to recognize a spoofed speech from a genuine one. Most of the current researches have focused on voice conversion (VC), synthesis and recapture which mimic a target speaker to break through ASV systems by increased false acceptance rates. However, there exists another type of spoofing, voice transformation (VT), that transforms a speech signal without a target in order ‘not to be recognized’ by increased false reject rates. VT has received much less attention.