Magnetic anomaly detection (MAD) is a technique applied in searching, localizing, and even tracking (if the target is moving) a ferromagnetic target of interest. Due to the complexity of the ambient magnetic field, almost all of the detection methods need a filter to be a preprocessing procedure. A typical way is passing the measured signal through a fixed frequency band that contains the frequency of the target signal. However, the target signal’s frequency is mostly determined by the movement velocity of the magnetometer and the distance from magnetometer to target. Namely, in different cases, the targets’ frequencies are different. We analyze the target model and represent the target signal with a single scalar variable. Through projecting the three-dimensional space into a two-dimensional plane, we lastly transform the target signal into a superposition of three sinusoids. Based on it, we propose a method to estimate the frequency band adaptively. Furthermore, we present an adaptive filtering method based on wavelet transform and take some simulation tests to prove that the proposed method has better performance compared with traditional filters.
Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. The existing algorithms are mainly graph based, which cast the reassembly problem as a K-vertex disjoint path problem in a directed complete graph, which is an NP-complete problem. Based on the padding bytes in BMP files, we present a method to exclude most impossible paths, which can improve the accuracy and decrease the time complexity of the existing graph-based methods. According to the alignment rule of BMP format, padding bytes must be appended to the end of each row to bring up the length of the row to a multiple of 4 bytes. Hence the fragment, being a vertex of the path which correctly reassembles a file, has a property; its byte values at padding positions must be the padding values. Only the fragments with such property can be candidate fragments for the vertex. On the test dataset which is constructed based on 330 image files, taking eight classical methods as examples, we show that the proposed method produces an accuracy improvement ranging from 32% to 55%, and reduces the run time to a scope from 1/6 to 1/237.
<i>Copy-move </i>is one of the most common methods for image manipulation. Several methods have been proposed to detect and locate the tampered regions, while many methods failed when the copied regions are rotated before being pasted. A rotational invariant detecting method using Polar Complex Exponential Transform (PCET) is proposed in this paper. Firstly, the original image is divided into overlapping circular blocks, and PCET is employed to each block to extract the rotation-invariant robust features. Secondly, the Approximate Nearest Neighbors (ANN) of each feature vector are collected by Locality Sensitive Hashing (LSH). Experimental results show that the proposed technique is robust to rotation.
The feature matching step plays a critical role during the copy-move forgery detection procedure. However, when several highly similar features simultaneously exist in the feature space, current feature matching methods will miss a considerable number of genuine matching feature pairs. To this end, we propose a clustering-based method to collect qualified matching features for the feature point-based methods. The proposed method can collect far more genuine matching features than existing methods do, and thus significantly improve the detection performance, especially for multiple pasting cases. Experimental results confirm the efficacy of the proposed method.