This paper presents a unified framework for identifying the source digital camera from its images and revealing digitally altered images using photo-response non-uniformity noise (PRNU), a unique stochastic fingerprint of imaging sensors. PRNU is obtained using a Maximum Likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising. The paper describes a simplified sensor output model used to derive the PRNU estimator and detector. A Maximum Likelihood estimator for the PRNU is derived and the need to preprocess the estimated signal to remove certain systematic patterns that might increase false alarms in Device Identification and missed detections in Integrity Verification is pointed out. The task of detecting the PRNU is formulated as a Neyman-Pearson hypothesis testing problem. The correlation predictor used to obtain the distribution of the test statistics is detailed. Benchmark implementation of the proposed framework for both forensic tasks and the performance evaluation appear in Section VI. The experimental results are accompanied with discussion of limitations and ideas for future research. The paper is summarized in Section VII. The PRNU factor is estimated using a Maximum Likelihood estimator derived from a simplified model of the sensor output. The estimated factor is then preprocessed to remove artifacts that are not unique to the camera sensor. The detection of PRNU is formulated as a hypothesis testing problem. The optimal detector for Camera Identification is the normalized Generalized Matched Filter. The detection of PRNU in the noise residual is formulated as a binary hypothesis testing problem. The correlation predictor is used to obtain the distribution of the test statistics under hypothesis H1. The correlation predictor is constructed as a mapping from some feature space to a real number in the interval [0,1]. The features that influence the correlation are image intensity, texture, and signal flattening. The correlation predictor is used to estimate the distribution of the test statistics under hypothesis H1. The experimental results show that the proposed framework is robust to common image processing. The paper also discusses the limitations of the proposed framework and suggests future research directions.This paper presents a unified framework for identifying the source digital camera from its images and revealing digitally altered images using photo-response non-uniformity noise (PRNU), a unique stochastic fingerprint of imaging sensors. PRNU is obtained using a Maximum Likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising. The paper describes a simplified sensor output model used to derive the PRNU estimator and detector. A Maximum Likelihood estimator for the PRNU is derived and the need to preprocess the estimated signal to remove certain systematic patterns that might increase false alarms in Device Identification and missed detections in Integrity Verification is pointed out. The task of detecting the PRNU is formulated as a Neyman-Pearson hypothesis testing problem. The correlation predictor used to obtain the distribution of the test statistics is detailed. Benchmark implementation of the proposed framework for both forensic tasks and the performance evaluation appear in Section VI. The experimental results are accompanied with discussion of limitations and ideas for future research. The paper is summarized in Section VII. The PRNU factor is estimated using a Maximum Likelihood estimator derived from a simplified model of the sensor output. The estimated factor is then preprocessed to remove artifacts that are not unique to the camera sensor. The detection of PRNU is formulated as a hypothesis testing problem. The optimal detector for Camera Identification is the normalized Generalized Matched Filter. The detection of PRNU in the noise residual is formulated as a binary hypothesis testing problem. The correlation predictor is used to obtain the distribution of the test statistics under hypothesis H1. The correlation predictor is constructed as a mapping from some feature space to a real number in the interval [0,1]. The features that influence the correlation are image intensity, texture, and signal flattening. The correlation predictor is used to estimate the distribution of the test statistics under hypothesis H1. The experimental results show that the proposed framework is robust to common image processing. The paper also discusses the limitations of the proposed framework and suggests future research directions.