Multi-Feature Fusion Based Deepfake Face Forgery Video Detection

With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library

Fast source camera identification using matching signs between query and reference fingerprints

Fast camera fingerprint search is an important issue for source camera identification in real-world applications. So far there has been little work done in this area. In this paper, we propose a novel fast search algorithm. We use global information derived from the relationship between the query fingerprint/digest and the reference fingerprints/digests in the database to guide fast search. This information can provide more accurate and robust clues for the selection of candidate matching database fingerprints. Because the quality of query fingerprints may degrade or vary in realistic applications, the construction of robust search clues is significant. To speed up the search process, we adopt a lookup table that is built on the separate-chaining hash table. The proposed algorithm has been tested using query images from real-world photos. Experiments demonstrate that our algorithm can well adapt to query fingerprints with different quality. It can achieve higher detection rates with lower computational cost than the traditional brute-force search algorithm and a pioneering fast search algorithm in literature

Fast camera fingerprint search algorithm for source camera identification

To determine the source camera of a query image, the fingerprint from the query image needs to be compared with the fingerprints in the reference fingerprint database. Traditionally, the query fingerprint is compared with these reference fingerprints one by one in sequence. For a large database, however, such a brute-force search is inefficient and time-consuming. How to accurately locate the correct fingerprint in the reference fingerprint database is thus becoming a crucial problem for commercial applications of source camera identification. So far there have been few studies in literature addressing this problem. In this work, we propose a new solution to fast fingerprint search. We first store the information of the reference fingerprint digests in the separate-chaining hash table, and then introduce a new rule to select the candidate reference fingerprint digests before performing the correlation. The selection rule is incarnated with the search priority vector. Experimental results have shown that the proposed algorithm outperforms current algorithms

Enhancing Performance of Lossy Compression on Encrypted Gray Images through Heuristic Optimization of Bitplane Allocation

Nowadays, it remains a major challenge to efficiently compress encrypted images. In this paper, we propose a novel encryption-then-compression (ETC) scheme to enhance the performance of lossy compression on encrypted gray images through heuristic optimization of bitplane allocation. Specifically, in compressing an encrypted image, we take a bitplane as a basic compression unit and formulate the lossy compression task as an optimization problem that maximizes the peak signal-to-noise ratio (PSNR) subject to a given compression ratio. We then develop a heuristic strategy of bitplane allocation to approximately solve this optimization problem, which leverages the asymmetric characteristics of different bitplanes. In particular, an encrypted image is divided into four sub-images. Among them, one sub-image is reserved, while the most significant bitplanes (MSBs) of the other sub-images are selected successively, and so are the second, third, etc., MSBs until a given compression ratio is met. As there exist clear statistical correlations within a bitplane and between adjacent bitplanes, where bitplane denotes those belonging to the first three MSBs, we further use the low-density parity-check (LDPC) code to compress these bitplanes according to the ETC framework. In reconstructing the original image, we first deploy the joint LDPC decoding, decryption, and Markov random field (MRF) exploitation to recover the chosen bitplanes belonging to the first three MSBs in a lossless way, and then apply content-adaptive interpolation to further obtain missing bitplanes and thus discarded pixels, which is symmetric to the encrypted image compression process. Experimental simulation results show that the proposed scheme achieves desirable visual quality of reconstructed images and remarkably outperforms the state-of-the-art ETC methods, which indicates the feasibility and effectiveness of the proposed scheme

microRNA-184 functions as tumor suppressor in renal cell carcinoma

microRNAs (miRNAs) are evolutionarily conserved, endogenous, small, noncoding RNA molecules of approximately 22 nucleotides in length that function as post-transcriptional gene regulators. Their aberrant expression may be involved in human diseases, including cancer. Although miRNA-184 (miR-184) has been reported in other tumors, its function in renal cell carcinoma (RCC) is still unknown. The aim of the present study was to investigate the role of miR-184 in RCC. The impacts of miR-184 on cell migration, proliferation and apoptosis were evaluated using migration scratch, 3-(4,5-dimethylthiazol-2-yl)2,5-diphenyltetrazolium bromide (MTT) and flow cytometry assay. Our studies revealed that miR-184 mimic significantly inhibits cell migration, suppresses cell proliferation and induces renal cancer cell apoptosis in vitro when compared with the negative control (P<0.05). In this study, it was observed that miR-184 played a significant role as a tumor suppressor in RCC. Therefore, miR-184 may be a promising therapeutic target for renal cancer treatment in the future

Identification of Hsf1 as a Novel Androgen Receptor-Regulated Gene in Mouse Sertoli Cells

Androgen signaling plays a crucial role in spermatogenesis, yet few downstream targets for this signaling pathway have been identified. In the current study, we found that the expression of heat-shock transcription factor 1 (Hsf1) was increased in the testes of Sertoli cell-selective androgen receptor knockout (S-AR(-/y)) mice compared with wild-type mice by quantitative real-time PCR, and the expression of HSF1 in the S-AR(-/y) Sertoli cells was significantly increased, based on immunofluorescence analysis. In vitro cell-culture studies showed that testosterone repressed the expression of Hsf1 in TM4 cells, a mouse Sertoli cell line. Moreover, a luciferase assay, electrophoretic mobility shift assay, and chromatin immunoprecipitation assay showed that testosterone repressed Hsf1 expression by facilitating the binding of androgen receptor to the Hsf1 promoter. Our experiments also demonstrated that testosterone-mediated inhibition of Hsf1 transcription down-regulated the expression of heat-shock proteins HSP105 and HSP60. Taken together, these results reveal that Hsf1 is a novel target of androgen receptor in mouse Sertoli cells, and testosterone and its receptor regulate the process of spermatogenesis partially by inhibiting Hsf1 expression. (C) 2014 Wiley Periodicals, Inc