Biblio

Investigations on the charge of possessing child pornography usually require manual forensic image inspection in order to collect evidence. When storage devices are confiscated, law enforcement authorities are hence often faced with massive image datasets which have to be screened within a limited time frame. As the ability to concentrate and time are highly limited factors of a human investigator, we believe that intelligent algorithms can effectively assist the inspection process by rearranging images based on their content. Thus, more relevant images can be discovered within a shorter time frame, which is of special importance in time-critical investigations of triage character. While currently employed techniques are based on black- and whitelisting of known images, we propose to use deep learning algorithms trained for the detection of pornographic imagery, as they are able to identify new content. In our approach, we evaluated three state-of-the-art neural networks for the detection of pornographic images and employed them to rearrange simulated datasets of 1 million images containing a small fraction of pornographic content. The rearrangement of images according to their content allows a much earlier detection of relevant images during the actual manual inspection of the dataset, especially when the percentage of relevant images is low. With our approach, the first relevant image could be discovered between positions 8 and 9 in the rearranged list on average. Without using our approach of image rearrangement, the first relevant image was discovered at position 1,463 on average.

This paper presents principles of Defensive Programming and examines the growing concern that these principles are not effectively incorporated into Computer Science and related computing degree programs' curricula. To support this concern, Defensive Programming principles are applied to a case study - Cross-site Scripting cybersecurity attacks. This paper concludes that Defensive Programming plays an important role in preventing these attacks and should thus be more aggressively integrated into CS courses such as Programming, Algorithms, Databases, Computer Architecture and Organization, and Computer Networks.

We propose an exact algorithm to model-free diagnosis with an application to fault localization in digital circuits. We assume that a faulty circuit and a correctness specification, e.g., in terms of an un-optimized reference circuit, are available. Our algorithm computes the exact set of all minimal diagnoses up to cardinality k considering all possible assignments to the primary inputs of the circuit. This exact diagnosis problem can be naturally formulated and solved using an oracle for Quantified Boolean Satisfiability (QSAT). Our algorithm uses Boolean Satisfiability (SAT) instead to compute the exact result more efficiently. We implemented the approach and present experimental results for determining fault candidates of digital circuits with seeded faults on the gate level. The experiments show that the presented SAT-based approach outperforms state-of-the-art techniques from solving instances of the QSAT problem by several orders of magnitude while having the same accuracy. Moreover, in contrast to QSAT, the SAT-based algorithm has any-time behavior, i.e., at any-time of the computation, an approximation of the exact result is available that can be used as a starting point for debugging. The result improves while time progresses until eventually the exact result is obtained.