Conference Paper

Detecting Video Forgeries Based on Noise Characteristics

DOI: 10.1007/978-3-540-92957-4_27 Conference: Advances in Image and Video Technology, Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
Source: DBLP


The recent development of video editing techniques enables us to create realistic synthesized videos. Therefore using video data as evidence in places such as a court of law requires a method to detect forged videos. In this paper we propose an approach to detect suspicious regions in video recorded from a static scene by using noise character- istics. The image signal contains irradiance-dependent noise where the relation between irradiance and noise depends on some parameters; they include inherent parameters of a camera such as quantum efficiency and a response function, and recording parameters such as exposure and elec- tric gain. Forged regions from another video camera taken under different conditions can be differentiated when the noise characteristics of the re- gions are inconsistent with the rest of the video.

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Available from: Yoichi Sato, Oct 01, 2015
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    • "In the technique proposed in [2] the basic idea is that, in a recompressed video the statistics of quantized or inverse quantized coefficients exhibit a deviation from that of original video. In [3] [4], noise characteristics are used to detect forgery. In [5], the authors detect double compression by capturing empty bins exhibited Fig. 1. "
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    ABSTRACT: Intelligent video editing techniques can be used to tamper videos such as surveillance camera videos, defeating their potential to be used as evidence in a court of law. In this paper, we propose a technique to detect forgery in MPEG videos by analyzing the frame's compression noise characteristics. The compression noise is extracted from spatial domain by using a modified Huber Markov Random Field (HMRF) as a prior for image. The transition probability matrices of the extracted noise are used as features to classify a given video as single compressed or double compressed. The experiment is conducted on different YUV sequences with different scale factors. The efficiency of our classification is observed to be higher relative to the state of the art detection algorithms.
    IEEE International Conference on Image Processing, Paris, France; 10/2014
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    • "When the noise intensities of the original and tampered regions are significantly different, it fails to reduce the noise accurately and can miss some forgeries because of the calculation error of noise residual. We proposed a forensic technique for tampered videos based on noise characteristics [13]. We extend our prior method as follows ; we assumed linear NLFs and estimated the NLF in the authentic region in a least squares manner, and then classified the pixels into authentic and tampered with respect to the distance from the estimated NLF. "
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    ABSTRACT: Recently developed video editing techniques have enabled us to create realistic synthesized videos. Therefore, using video data as evidence in places such as courts of law requires a method to detect forged videos. In this study, we developed an approach to detect suspicious regions in a video of a static scene on the basis of the noise characteristics. The image signal contains irradiance-dependent noise the variance of which is described by a noise level function (NLF) as a function of irradiance. We introduce a probabilistic model providing the inference of an NLF that controls the characteristics of the noise at each pixel. Forged pixels in the regions clipped from another video camera can be differentiated by using maximum a posteriori estimation for the noise model when the NLFs of the regions are inconsistent with the rest of the video. We demonstrate the effectiveness of our proposed method by adapting it to videos recorded indoors and outdoors. The proposed method enables us to highly accurately evaluate the per-pixel authenticity of the given video, which achieves denser estimation than prior work based on block-level validation. In addition, the proposed method can be applied to various kinds of videos such as those contaminated by large noise and recorded with any scan formats, which limits the applicability of the existing methods.
    IEEE Transactions on Information Forensics and Security 01/2011; 5(4-5):883 - 892. DOI:10.1109/TIFS.2010.2074194 · 2.41 Impact Factor
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    ABSTRACT: In the digital multimedia era, it is increasingly important to ensure the integrity and authenticity of the vast volumes of video data. A novel approach is proposed for detecting video forgery based on ghost shadow artifact in this paper. Ghost shadow artifact is usually introduced when moving objects are removed by video inpainting. In our approach, ghost shadow artifact is accurately detected by inconsistencies of the moving foreground segmented from the video frames and the moving track obtained from the accumulative frame differences, thus video forgery is exposed. Experiments show that our approach achieves promising results in video forgery detection.
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