# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
Article [53] investigates the use of anti-forensics for the creation of fake personal profiles and proposes defense strategies by which to improve the robustness of forensic systems. On the other hand, papers [52; 54] presents a CG anti-forensic facial image regeneration scheme, achieving a balance between visual quality and deception capability. Taken together, these studies represent a significant advance in the field of DeepFakes anti-forensics, offering new strategies and models with which to improve visual quality and evade forensic detection. * [PERSON] [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66] discuss techniques for tampering detection using median filtering (MF) on JPEG images. [PERSON] et al. [67; 68] describe a method for improving the quality of median-filtered (MF) images while making such manipulation more difficult to detect forensically. Paper [57] proposes a technique by which to detect blocking artifacts in JPEG images, while [58] introduces a forensic fingerprint based on residual median filtering. Paper [59] focuses on anti-forensic techniques to counter contrast enhancement detectors in digital images, and [60] presents an anti-forensic attack method for median filtering by adding uniformly distributed noise. Paper [61] proposes new variants of contrast enhancement operators that are undetectable by existing contrast enhancement detectors, while [62,66] analyzes the traces left by anti-forensic techniques for median filtering and proposes an anti-forensic method. Paper [63] proposes a robust residual median filtering residual difference domain (MFRD)-based detector; on the other hand, [64] proposes a two-stage median filtering anti-forensic framework to hide median filtering artifacts in JPEG images, while [65] presents an anti-forensic technique to counteract contrast enhancement detection in digital images. Paper [67] introduces a variational convolution framework for the quality enhancement and anti-forensics of median filtered images. On the other hand, [68] proposes an anti-forensic technique that hides traces left by median filtering while preserving the quality of the processed image. As novel contributions, papers [57,58] propose methods by which to detect JPEG compression artifacts and JPEG blocking, respectively. Articles [59,60] present anti-forensic techniques for improving contrast enhancement detection and median filtering, while [61] introduces undetectable contrast enhancement operators. Article [62] analyzes and proposes an anti-forensic method for median filtering. In addition, Refs. [63,68] provide novel approaches to detect and counteract median filtering-based image manipulations, including robust detection methods, two-stage anti-forensic frameworks, and variational convolution techniques. * A considerable number of authors, such as [PERSON] et al. [69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], cover techniques for the detection of anti-forensics based on image compression. [PERSON] et al. [106] do the same using CNNs. Authors such as [PERSON] et al. [107,108] apply it to double image compression. [PERSON] et al. [109] focus their study on the double compression of JPG images and also base it on GAN. Articles [69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 103, 104, 105] propose anti-forensic methods by which to remove JPEG compression fingerprints and make it difficult to detect image manipulation, while articles [75,78,105,106] propose methods by which to identify images that have undergone anti-forensic processing. Articles [76,107] propose anti-forensic methods based on Benford's law, and articles [78,109] propose anti-forensic methods based on the generative adversarial network (GAN), which offer better performance and image quality than the previous methods.