Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. Therefore, we categorized our data into three main and essential categories: the first groups mainly need firefighting service, while the second and third categories are about thefts and violent behaviour. We implemented the proposed method on the UCF-Crime dataset and achieved 81.71% in AUC, higher than other models like C3D on the same dataset. Our future work focuses on adding an attention layer to the existing model to detect more abnormal events.

We present adversarial event prediction (AEP), a novel approach to detecting abnormal events through an event prediction setting. Given normal event samples, AEP derives the prediction model, which can discover the correlation between the present and future of events in the training step. In obtaining the prediction model, we propose adversarial learning for the past and future of events. The proposed adversarial learning enforces AEP to learn the representation for predicting future events and restricts the representation learning for the past of events. By exploiting the proposed adversarial learning, AEP can produce the discriminative model to detect an anomaly of events without complementary information, such as optical flow and explicit abnormal event samples in the training step. We demonstrate the efficiency of AEP for detecting anomalies of events using the UCSD-Ped, CUHK Avenue, Subway, and UCF-Crime data sets. Experiments include the performance analysis depending on hyperparameter settings and the comparison with existing state-of-the-art methods. The experimental results show that the proposed adversarial learning can assist in deriving a better model for normal events on AEP, and AEP trained by the proposed adversarial learning can surpass the existing state-of-the-art methods.

With the mushroom growth of state‐of‐the‐art digital image and video manipulations tools, establishing the authenticity of multimedia content has become a challenging issue. Digital image forensics is an increasingly growing research field that symbolises a never ending struggle against forgery and tampering. This survey attempts to cover the blind techniques that have been proposed for exposing forgeries. This work dwells on the detection techniques for three of the most common forgery types, namely copy/move, splicing and retouching.

Interpretation of the reasoning process of a prediction made by a deep learning model is always desired. However, when it comes to the predictions of a deep learning model that directly impacts on the lives of people then the interpretation becomes a necessity. In this paper, we introduce a deep learning model: negative-positive prototypical part network (NP-ProtoPNet). This model attempts to imitate human reasoning for image recognition while comparing the parts of a test image with the corresponding parts of the images from known classes. We demonstrate our model on the dataset of chest X-ray images of Covid-19 patients, pneumonia patients and normal people. The accuracy and precision that our model receives is on par with the best performing non-interpretable deep learning models.