Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100 accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.
Villalba et al @cite_24 presented a method for video source acquisition identification based on sensor noise extraction from video key frames. Photo response non-uniformity (PRNU) is the primary part of the sensor pattern noise in an image. @cite_24 , the PRNU is used to calculate the sensor pattern noise and characterize the fingerprints into feature vectors. Then, the feature vectors are extracted from the video key frames and trained by a SVM-based classifier.
@cite_24
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Every day the use of videos from mobile devices as evidence in legal proceedings is more usual and common.Image source acquisition identification is a branch of digital forensic analysis.We use of sensor pattern noise for source identification.We make a series of experiments which emulate similar situations to those that may occur in reality. Recording videos on smartphones and other mobile devices, given their enormous popularity, is currently very common. The portability of these devices facilitates their use for recording videos in a wide variety of situations, including while witnessing criminal activities. These videos can be later used as evidence in legal proceedings. Therefore, the forensic analysis of videos taken with mobile device videos is important, and could serve for legal and also investigative purposes. It is necessary, however, to use techniques that are quite specific to this type of devices, given some peculiar features of their cameras. In this paper, we will address the issue of video source acquisition identification by presenting a technique based on sensor noise and wavelet transform extraction from video key frames. These frames are extracted using an efficient algorithm that takes their content into account, improving the selection of frames to be analyzed over past proposals. The scheme presented consists of four stages: (1) Key frames extraction, (2) sensor pattern noise extraction, (3) feature extraction, and (4) classifier training and prediction. We also present experimental results that support the validity of the techniques used and show promising results. In this paper we propose a novel method for human action recognition based on boosted key-frame selection and correlated pyramidal motion feature representations. Instead of using an unsupervised method to detect interest points, a Pyramidal Motion Feature (PMF), which combines optical flow with a biologically inspired feature, is extracted from each frame of a video sequence. The AdaBoost learning algorithm is then applied to select the most discriminative frames from a large feature pool. In this way, we obtain the top-ranked boosted frames of each video sequence as the key frames which carry the most representative motion information. Furthermore, we utilise the correlogram which focuses not only on probabilistic distributions within one frame but also on the temporal relationships of the action sequence. In the classification phase, a Support-Vector Machine (SVM) is adopted as the final classifier for human action recognition. To demonstrate generalizability, our method has been systematically tested on a variety of datasets and shown to be more effective and accurate for action recognition compared to the previous work. We obtain overall accuracies of: 95.5 , 93.7 , and 36.5 with our proposed method on the KTH, the multiview IXMAS and the challenging HMDB51 datasets, respectively. This paper addresses a spatiotemporal pattern recognition problem. The main purpose of this study is to find a right representation and matching of action video volumes for categorization. A novel method is proposed to measure video-to-video volume similarity by extending Canonical Correlation Analysis (CCA), a principled tool to inspect linear relations between two sets of vectors, to that of two multiway data arrays (or tensors). The proposed method analyzes video volumes as inputs avoiding the difficult problem of explicit motion estimation required in traditional methods and provides a way of spatiotemporal pattern matching that is robust to intraclass variations of actions. The proposed matching is demonstrated for action classification by a simple Nearest Neighbor classifier. We, moreover, propose an automatic action detection method, which performs 3D window search over an input video with action exemplars. The search is speeded up by dynamic learning of subspaces in the proposed CCA. Experiments on a public action data set (KTH) and a self-recorded hand gesture data showed that the proposed method is significantly better than various state-of-the-art methods with respect to accuracy. Our method has low time complexity and does not require any major tuning parameters. Key frame extraction is an important technique in video summarization, browsing, searching and understanding. In this paper, we propose a novel approach to extract the most attractive key frames by using a saliency-based visual attention model that bridges the gap between semantic interpretation of the video and low-level features. First, dynamic and static conspicuity maps are constructed based on motion, color and texture features. Then, by introducing suppression factor and motion priority schemes, the conspicuity maps are fused into a saliency map that includes only true attention regions to produce attention curve. Finally, after time-constraint cluster algorithm grouping frames with similar content, the frames with maximum saliency value are selected as key-frames. Experimental results demonstrate the effectiveness of our approach for video summarization by retrieving the meaningful key frames.