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FISH: Face intensity-shape histogram representation for automatic face splicing detection

机译:鱼:人脸强度形状直方图表示,用于自动人脸拼接检测

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摘要

Tampered images spread nowadays over any visual media influencing our judgement in many aspects of our life. This is particularly critical for face splicing manipulations, where recognizable identities are put out of context. To contrast these activities on a large scale, automatic detectors are required.In this paper, we present a novel method for automatic face splicing detection, based on computer vision, that exploits inconsistencies in the lighting environment estimated from different faces in the scene. Differently from previous approaches, we do not rely on an ideal mathematical model of the lighting environment. Instead, our solution, built upon the concept of histogram-based features, is able to statistically represent the current interaction of faces with light, untied from the actual and unknown reflectance model. Results show the effectiveness of our solution, that outperforms existing approaches on real-world images, being more robust to face shape inaccuracies. (C) 2019 Elsevier Inc. All rights reserved.
机译:如今,篡改的图像散布在影响我们生活各个方面的判断的任何视觉媒体上。这对于面部识别操作特别重要,因为面部识别操作无法识别上下文。为了大规模地对比这些活动,需要使用自动检测器。在本文中,我们提出了一种基于计算机视觉的自动人脸拼接检测新方法,该方法利用了从场景中不同人脸所估计的照明环境中的不一致性。与以前的方法不同,我们不依赖照明环境的理想数学模型。取而代之的是,我们的解决方案基于基于直方图的特征的概念,能够从实际和未知的反射率模型中统计地表示人脸与光线的当前交互。结果显示了我们的解决方案的有效性,它优于现实世界图像上的现有方法,并且对于面部形状不准确的情况更可靠。 (C)2019 Elsevier Inc.保留所有权利。

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