Clothing identification via deep learning:forensic applications

Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals.An item such as clothing is a visual attribute because it can usually be used to describe people.The method proposed in this article aims to identify people based on the visual information derived from their attire.Deep learning is used to train the computer to classify images based on clothing content.We first demonstrate clothing classification using a large scale dataset,where the proposed model performs relatively poorly.Then,we use clothing classification on a dataset containing popular logos and famous brand images.The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%.Finally,we evaluate clothing classification using footage from surveillance cameras.The system performs well on this dataset,labelling 70%of the test images correctly.

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.