# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases  
This section, represented in Figures 10 and 11, groups the papers whose main theme is to expose the weaknesses of a domain and the related anti-forensic use cases. A multitude of papers could be included in several sections; therefore, the main criterion for their grouping is, more specifically, main objective sought by the authors. Figure 8: Publications grouped by section (percentages). Figure 9: Publications by section and number of articles. of vanguard publications in different areas. [PERSON] et al. [22; 23] add a method for the general detection of these techniques using convolutional neural networks (CNNs). On the other hand, [PERSON] et al. [24; 25; 26; 27; 28; 29] attempt to detect contrast enhancement (CE) techniques. [PERSON] et al. [30; 31] use CNNs to detect CE techniques. Several studies [22; 23; 31; 32] use neural networks (CNNs and GANs) to detect or generate forensic images, demonstrating their effectiveness in learning forensic traces and generating sophisticated anti-forensic attacks. Other approaches [25; 26] are based on the analysis of first- and second-order statistics and Laplacian modeling to detect contrast enhancement and anti-forensic techniques. Methods based on CNN [22; 31; 32] and GAN (generative adversarial networks) [23; 29] show high accuracy and robustness in detecting forgery and anti-forensic attacks. Anti-forensic approaches [25; 29] focus on minimizing distortion and maintaining image quality, while others [26; 27] identify anomalies introduced by anti-forensic techniques. The integration of GANs and CNNs represents a significant advance in the detection and generation of digital image forgeries. In addition, studies that optimize anti-forensic and forensic approaches [25; 32] contribute to improving the accuracy and efficiency of existing methods. * [PERSON] et al. [32] propose a method for evaluating the performance of forensic systems that analyze the tampering of an image. [PERSON] et al. [33] propose a theoretical framework based on the Dempster-Shafer theory of evidence to merge the information provided by forensic tools and anti-forensic tools. Both papers focus on improving the detection of manipulated images but from different perspectives. The article [32] proposes a model by which to evaluate the security of forensic techniques, considering both omission and false alarm errors. On the other hand, paper [33] focuses on countering anti-forensic techniques by fusing data from traditional forensic tools and CAF (computer-aided forensic) tools. Both studies agree that security and efficiency are crucial components of the forensic analysis of manipulated images. The main differences between the articles lie in their objectives (performance evaluation vs. countering anti-forensic techniques), the methodologies employed (hypothesis testing model vs. data fusion based on Dempster-Shafer theory), and the aspects they emphasize (security against false alarms vs. relationships between forensic and CAF techniques). Regarding novel contributions, paper [32] highlights the importance of security against false alarms in forensic evaluation, while paper [33] proposes a data fusion framework to counteract anti-forensic techniques. * [PERSON] et al. [34] examine the anti-forensic techniques applied to color interpolation in digital images. [PERSON] et al. [35] construct a review by applying it to image sharpening. [PERSON] et al. [36] performs the same review using CNN. All three papers focus on anti-forensic techniques with which to hinder tampering detection in digital images. Article [34] evaluates the robustness of color interpolation identification against two anti-forensic techniques, while article [35] focuses on a specific anti-forensic scheme for USM sharpening. On the other hand, paper [36] questions the effectiveness of CNN-based sharpening detectors and proposes a GAN-based anti-forensic method.