Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire

Copy-move forgery detection in digital images

The ready availability of image-editing software makes it important to ensure the authenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, in which part(s) of the image are copied and pasted back somewhere else in the same image. Post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. This thesis presents three novel methods for CMF detection, using feature extraction, surface fitting and segmentation. The Dense Scale Invariant Feature Transform (DSIFT) has been improved by using a different method to estimate the canonical orientation of each circular block. The Fitting Function Rotation Invariant Descriptor (FFRID) has been developed by using the least squares method to fit the parameters of a quadratic function on each block curvatures. In the segmentation approach, three different methods were tested: the SLIC superpixels, the Bag of Words Image and the Rolling Guidance filter with the multi-thresholding method. We also developed the Segment Gradient Orientation Histogram (SGOH) to describe the gradient of irregularly shaped blocks (segments). The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion and combined transformation). Moreover, the proposed methods are robust to post-processing (i.e. blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects. In addition, we developed a new method to estimate the similarity threshold for each image by optimizing a cost function based probability distribution. This method can detect CMF better than using a fixed threshold for all the test images, because our proposed method reduces the false positive and the time required to estimate one threshold for different images in the dataset. Finally, we used the hysteresis to decrease the number of false matches and produce the best possible result

Designing Robust Collaborative Services in Distributed Wireless Networks

Wireless Sensor Networks (WSNs) are a popular class of distributed collaborative networks finding suitability from medical to military applications. However, their vulnerability to capture, their "open" wireless interfaces, limited battery life, all result in potential vulnerabilities. WSN-based services inherit these vulnerabilities. We focus on tactical environments where sensor nodes play complex roles in data sensing, aggregation and decision making. Services in such environments demand a high level of reliability and robustness. The first problem we studied is robust target localization. Location information is important for surveillance, monitoring, secure routing, intrusion detection, on-demand services etc. Target localization means tracing the path of moving entities through some known surveillance area. In a tactical environment, an adversary can often capture nodes and supply incorrect surveillance data to the system. In this thesis we create a target localization protocol that is robust against large amounts of such falsified data. Location estimates are generated by a Bayesian maximum-likelihood estimator. In order to achieve improved results with respect to fraudulent data attacks, we introduce various protection mechanisms. Further, our novel approach of employing watchdog nodes improves our ability to detect anomalies reducing the impact of an adversarial attack and limiting the amount of falsified data that gets accepted into the system. By concealing and altering the location where data is aggregated, we restrict the adversary to making probabilistic "guess" attacks at best, and increase robustness further. By formulating the problem of robust node localization under adversarial settings and casting it as a multivariate optimization problem, we solve for the system design parameters that correspond to the optimal solution. Together this results in a highly robust protocol design. In order for any collaboration to succeed, collaborating entities must have the same relative sense of time. This ensures that any measurements, surveillance data, mission commands, etc will be processed in the same epoch they are intended to serve. In most cases, data disseminated in a WSN is transient in nature, and applies for a short period of time. New data routinely replaces old data. It is imperative that data be placed in its correct time context; therefore..