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This chapter covers drawing support and resistance lines to highlight the behavior within a trading range.

Digital images serve as crucial evidence in fields like forensics and medicine, yet their reliability is increasingly threatened by sophisticated forgery techniques such as copy-move. While traditional block-based, keypoint-based, or deep learning approaches exist, each faces challenges regarding geometric transformations, dataset dependency, or computational complexity. This paper presents a novel hybrid model, HDBK, integrating deep learning, block-based, and keypoint-based methods for forgery detection at both image and pixel levels. The proposed framework comprises three fundamental stages. First, a triple-architecture ensemble of deep learning networks identifies forgery images and generates localized heatmaps to highlight suspected regions. Next, an enhanced block-based method utilizing a Genetic Algorithm (GA) is employed. This stage uses the maximum number of matched keypoints between candidate blocks as a fitness function to identify regions similar to the suspected forgery blocks. Finally, keypoint descriptors (SIFT, SURF, and FAST) are applied to match features between the identified regions, achieving precise pixel-level localization. This hybrid approach effectively reduces the search space for optimization, enhancing accuracy while minimizing false positive rates. The HDBK model was rigorously evaluated on the CoMoFoD dataset, which includes forgeries under various geometric transformations and post-processing operations like blurring and JPEG compression. Experimental results demonstrate that the model outperforms state-of-the-art techniques, particularly in detecting challenging scenarios such as small-scale and smooth forgery regions. The synergy between deep feature maps and meta-heuristic optimization ensures a robust balance between computational efficiency and forensic integrity in real-world passive forensic applications.

The copy-move forgery refers to the copying and pasting of a region of the original image into the target region of the same image, which represents a typical tampering method with the characteristics of easy tampering and high-quality tampering. The existing single feature-based methods of forgery detection have certain shortcomings, such as high false alarm rate, low robustness, and low detection accuracy. To address these shortcomings, this paper proposes an improved two-stage detection method based on parallel feature fusion and an adaptive threshold generation algorithm. Firstly, the SLIC super-pixels segmentation algorithm is used for image preprocessing, and a similar region extraction algorithm without threshold is employed to obtain the suspected tampering regions with high similarity. Secondly, the parallel fusion feature is obtained based on the SIFT and HU features to express the characteristics of local regions. Then, the corresponding threshold value is generated based on the histogram of oriented gradient (HOG) to describe the texture characteristics of the obtained regions, which acts as a criterion to judge whether a region has been forged or not. The experimental results show that the proposed method outperforms the existing methods, achieving the accuracy of 99.01% and 98.5% on the MICC-F220 and MICC-F2000 datasets respectively. In addition, the proposed method has stronger robustness performance on COMOFOD dataset than the comparison methods.

Digital video is critical visual evidence in various fields and is easily manipulated under different techniques such as the popular video copy-move forgery. In the past decades, although machine intelligence has been widely adopted to detect the forgery in digital images automatically, It still remains a very challenging detection task for carefully-crafted copy-move forgery in digital video for three reasons: (i) A video of medium length containing hundreds of frames already incurs a prohibitive computational cost; (ii) Similar backgrounds in contiguous frames are easily mistakenly detected as copy-move forgery regions, resulting to a large number of false alarms; (iii) Most state-of-the-art methods cannot detect video copy-move inter-frame or intra-frame forgeries; To effectively address these issues, a fast forgery frame detection method for video copy-move inter/intra-frame identification is proposed: (i) The sparse feature extraction and matching speed-up the algorithm processing and reduce the time cost greatly (Defect (i)); (ii) The adaptive two-pass filtering and copy-move frame-pair matching can address the similarity problem (Defect (ii)) to locate truly forgery frame-pairs (FFP); (iii) Based on the results of these FFP, the type of video copy-move forgery detection can be identified (Defect (iii)). Furthermore, the copy-move frame-pair matching algorithm locates truly FFP, thus further reducing the computation cost and false alarm for detecting the inter/intra-frame forgery efficiently and effectively (Defect (i)). Finally, based on the truly FFP, the video can be checked for forgery or original. If there is no truly FFP, the video is considered as the original one. Otherwise, the video is checked if the forgery is inter-frame (i.e., truly FFP frames are two different frames) or intra-frame (the same frame). The experimental results show that our proposed algorithm achieves higher detection accuracy and higher robustness ( false alarm  = 2 and F 1  = 0.90) in the whole GRIP dataset than the existing state-of-the-art methods under various adverse conditions.

This chapter discusses the features of Foucault's approach that serve to justify Veyne's characterization—namely, his nominalism. Foucault pushes to the extreme the nominalist proclivities of historians to attend to the singular and nonrepeatable. This tendency respects the empirical and suspects the abstract. It also inverts the received “causal” accounts in a Nietzschean move to free us from the tyranny of false causes and vague relationships, such as the concept of influence, so prevalent in the history of ideas. Nominalism leads him, for example, to claim that “power” as such does not exist; there are only individual instances of action on the action of others.