Forensic Camera Identification in Social Networks via Camera Fingerprint

Tzu-Yun Lin, Yu-Ru Wang
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引用次数: 1

Abstract

Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.
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基于摄像头指纹的社交网络法医摄像头识别
图像犯罪引发了对数字图像溯源的迫切需求。突破口是Lukáš等人提出的通过光响应非均匀性(PRNU)分析的被动检测方法。最近,数码图像通常是用手持设备(如智能手机)拍摄的,并通过社交媒体(如LINE)传播。在传输过程中,大多数图像都是扭曲的(如压缩和调整大小)。以往的研究较少关注通过社交网络传输压缩的影响。在实验中,31款不同的苹果手机被用来捕捉数字图像。照片通过LINE软件上传到相册,然后下载。利用改进的符号峰值相关能(MSPCE)统计量来评估争议图像的PRNU值与实验设备的模式噪声之间的相关性。实验结果表明,PRNU分析方法可以有效地利用在LINE传输过程中压缩和调整大小的畸变图像来跟踪射击装置的源。
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