Detection of re-compression, transcoding and frame-deletion for digital video authentication

R. D. Singh, N. Aggarwal
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引用次数: 8

Abstract

In the wake of widespread surfeit of inexpensive and user-friendly digital multimedia alteration software, digital images and videos have lost the unparalleled position they once occupied as `authoritative testament of occurrence of events'. The inherent susceptibility of digital content to malevolent manipulations renders it vulnerable to our skepticism. Establishment of authenticity of digital content is of utmost importance in situations where reliability on fraudulent evidence could have serious consequences. With the intent of tackling a few of the several challenges of the video forensics domain, in this paper we propose a potent DCT coefficient analysis-based forensic technique for reliable detection of re-compression and transcoding in digital videos. This scheme facilitates visually perceptible differentiation between singly-compressed and re-compressed video frames while circumventing the need for undertaking any complicated peak periodicity analysis procedures that are normally associated with traditional DCT-based studies. We also present a unique optical-flow analysis scheme, where, instead of inspecting inconsistencies caused by frame-removal in the entire optical flow sequences of a given video, we focus entirely on the brightness gradient component of this flow. The experiments in this regard substantiate the forensic capabilities of this component and proffer observations conducive to the detection and localization of frame-removal in digital videos. Subjective and quantitative experimentation on a comprehensive dataset under a wide range of experimental set-ups validate the efficacy and resilience of the proposed techniques.
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数字视频认证中的重压缩、转码和删帧检测
随着廉价和用户友好的数字多媒体修改软件的广泛泛滥,数字图像和视频已经失去了它们曾经占据的“事件发生的权威证明”的无与伦比的地位。数字内容对恶意操纵的固有敏感性使其容易受到我们的怀疑。在欺诈证据的可靠性可能造成严重后果的情况下,建立数字内容的真实性至关重要。为了解决视频取证领域的一些挑战,在本文中,我们提出了一种有效的基于DCT系数分析的取证技术,用于可靠地检测数字视频中的再压缩和转码。该方案促进了单压缩和再压缩视频帧之间的视觉上可感知的区分,同时避免了通常与传统的基于dct的研究相关的任何复杂的峰值周期性分析过程的需要。我们还提出了一种独特的光流分析方案,其中,而不是检查在给定视频的整个光流序列中由帧去除引起的不一致性,我们完全关注该流的亮度梯度成分。这方面的实验证实了该组件的取证能力,并提供了有助于检测和定位数字视频中的帧删除的观察结果。在广泛的实验设置下对综合数据集进行的主观和定量实验验证了所提出技术的有效性和弹性。
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