论文标题
PRNU重点:乘法模型的概括
PRNU Emphasis: a Generalization of the Multiplicative Model
论文作者
论文摘要
光响应非均匀性(PRNU)是一种摄像机特异性模式,广泛用于解决多媒体取证问题,例如设备识别或伪造检测。该指纹的理论分析通常取决于降级残差的乘法模型。该设置假设从场景辐照度到预处理亮度的非线性映射,即使用光学和数字预处理管道的相机响应函数(CRF)组成,是伽马校正。但是,这个假设在实践中很少存在。在这封信中,我们通过包括该非线性映射对降级残差的影响来改善乘法模型。我们还提出了一种估计这种效果的方法。结果证据表明,典型相机的响应与伽马校正偏离。使用我们的模型的实验设备识别将TPR提高了$ 4.93 \,\%$,固定FPR为0.01美元。
The photoresponse non-uniformity (PRNU) is a camera-specific pattern, widely adopted to solve multimedia forensics problems such as device identification or forgery detection. The theoretical analysis of this fingerprint customarily relies on a multiplicative model for the denoising residuals. This setup assumes that the nonlinear mapping from the scene irradiance to the preprocessed luminance, that is, the composition of the Camera Response Function (CRF) with the optical and digital preprocessing pipelines, is a gamma correction. Yet, this assumption seldom holds in practice. In this letter, we improve the multiplicative model by including the influence of this nonlinear mapping on the denoising residuals. We also propose a method to estimate this effect. Results evidence that the response of typical cameras deviates from a gamma correction. Experimental device identification with our model increases the TPR by a $4.93\, \%$ on average for a fixed FPR of $0.01$.