A study of detecting child pornography on smart phone

© Springer International Publishing AG 2018. Child Pornography is an increasingly visible rising cybercrime in the world today. Over the past decade, with rapid growth in smart phone usage, readily available free Cloud Computing storage, and various mobile communication apps, child pornographers have found a convenient and reliable mobile platform for instantly sharing pictures or videos of children being sexually abused. Within this new paradigm, law enforcement officers are finding that detecting, gathering, and processing evidence for the prosecution of child pornographers is becoming increasingly challenging. Deep learning is a machine learning method that models high-level abstractions in data and extracts hierarchical representations of data by using a deep graph with multiple processing layers. This paper presents a conceptual model of deep learning approach for detecting child pornography within the new paradigm by using log analysis, file name analysis and cell site analysis which investigate text logs of events that have happened in the smart phone at the scene of the crime using physical and logical acquisition to assists law enforcement officers in gathering and processing child pornography evidence for prosecution. In addition, this paper shows an illustrative example of logical and physical acquisition on smart phones using forensics tools

Speed up interactive image retrieval

In multimedia retrieval, a query is typically interactively refined towards the “optimal” answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. Furthermore, it may also take too many iterations to get the “optimal” answers. In this paper, we introduce a new approach called OptRFS (optimizing relevance feedback search by query prediction) for iterative relevance feedback search. OptRFS aims to take users to view the “optimal” results as fast as possible. It optimizes relevance feedback search by both shortening the searching time during each iteration and reducing the number of iterations. OptRFS predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses (i.e., random accesses) can be saved, hence reducing the searching time for the next iteration. In addition, efficient scan on the overlap before the next search starts also tightens the search space with smaller pruning radius. As a step forward, OptRFS also predicts the “optimal” query, which corresponds to “optimal” answers, based on the early executed iterations’ queries. By doing so, some intermediate iterations can be saved, hence reducing the total number of iterations. By taking the correlations among the early executed iterations into consideration, OptRFS investigates linear regression, exponential smoothing and linear exponential smoothing to predict the next refined query so as to decide the overlap of candidates between two consecutive iterations. Considering the special features of relevance feedback, OptRFS further introduces adaptive linear exponential smoothing to self-adjust the parameters for more accurate prediction. We implemented OptRFS and our experimental study on real life data sets show that it can reduce the total cost of relevance feedback search significantly. Some interesting features of relevance feedback search are also discovered and discussed