Camera recognition with deep learning

In this paper,camera recognition with the use of deep learning technique is introduced.To identify the various cameras,their characteristic photo-response non-uniformity(PRNU)noise pattern was extracted.In forensic science,it is important,especially for child pornography cases,to link a photo or a set of photos to a specific camera.Deep learning is a sub-field of machine learning which trains the computer as a human brain to recognize similarities and differences by scanning it,in order to identify an object.The innovation of this research is the use of PRNU noise patterns and a deep learning technique in order to achieve camera identification.In this paper,AlexNet was modified producing an improved training procedure with high maximum accuracy of 80%–90%.DIGITS showed to have identified correctly six cameras out of 10 with a success rate higher than 75%in the database.However,many of the cameras were falsely identified indicating a fault occurring during the procedure.A possible explanation for this is that the PRNU signal is based on the quality of the sensor and the artefacts introduced during the production process of the camera.Some manufacturers may use the same or similar imaging sensors,which could result in similar PRNU noise patterns.In an attempt to form a database which contained different cameras of the same model as different categories,the accuracy rate was low.This provided further proof of the limitations of this technique,since PRNU is stochastic in nature and should be able to distinguish between different cameras from the same brand.Therefore,this study showed that current convolutional neural networks(CNNs)cannot achieve individualization with PRNU patterns.Nevertheless,the paper provided material for further research.

Deep Feature Learning for Intrinsic Signature Based Camera Discrimination

In this paper we consider the problem of“end-to-end”digital camera identification by considering sequence of images obtained from the cameras.The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digital conversion smooths out the intrinsic noise in the analog signal.However it is known that identifying a digital camera is possible by analyzing the camera’s intrinsic sensor artifacts that are introduced into the images/videos during the process of photo/video capture.It is known that such methods are computationally intensive requiring expensive pre-processing steps.In this paper we propose an end-to-end deep feature learning framework for identifying cameras using images obtained from them.We conduct experiments using three custom datasets:the first containing two cameras in an indoor environment where each camera may observe different scenes having no overlapping features,the second containing images from four cameras in an outdoor setting but where each camera observes scenes having overlapping features and the third containing images from two cameras observing the same checkerboard pattern in an indoor setting.Our results show that it is possible to capture the intrinsic hardware signature of the cameras using deep feature representations in an end-to-end framework.These deep feature maps can in turn be used to disambiguate the cameras from each another.Our system is end-to-end,requires no complicated pre-processing steps and the trained model is computationally efficient during testing,paving a way to have near instantaneous decisions for the problem of digital camera identification in production environments.Finally we present comparisons against the current state-of-the-art in digital camera identification which clearly establishes the superiority of the end-to-end solution.