.png){kind=icon}
返回
Reliable Camera Model Identification Using Sparse Gaussian Processes
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2021.3070206.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Identifying the model of a camera that has captured an image can be an important task in criminal investigations. Many methods assume that the image under analysis originates from a given set of known camera models. In practice, however, a photo can come from an unknown camera model, or its appearance could have been altered by unknown post-processing. In such a case, forensic detectors are prone to fail silently. One way to mitigate silent failures is to use a rejection mechanism for unknown examples. In this work, we propose Gaussian processes (GPs), which intrinsically provide such a rejection mechanism. This makes GPs a potentially powerful tool in multimedia forensics, where forensic analysts regularly work on images from unknown origins. We demonstrate that GPs scale well to the task of camera model identification. Probabilistic predictions from a GP classifier achieve high classification accuracy for known camera models while providing reliable uncertainty estimates. The built-in uncertainty estimates effectively tackle open-set camera model identification, outperforming two state-of-the-art methods.
Keyword:
Cameras
Training
Support vector machines
Kernel
Data models
Uncertainty
Gaussian processes
Camera model identification
Gaussian processes
open-set classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Post-Translational Modifications to Cysteine Residues in Plant Proteins and Their Impact on the Regulation of Metabolism and Signal Transduction植物蛋白质中半胱氨酸残基的翻译后修饰及其对代谢和信号转导的调控
OAAI
OAAI分享收藏
查看更多内容