# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
Regarding the differences, the aim of article [34] is to evaluate robustness, that of article [35] is to invalidate existing forensic methods, and that of article [36] is to question the effectiveness of CNN detectors. Regarding manipulation, article [34] addresses color interpolation, article [35] focuses on USM sharpening, and article [36] addresses sharpening in general. In terms of methodology, paper [34] employs parameter perturbation and algorithm mixing, paper [35] uses artifact removal, and paper [36] is based on generative antagonistic networks (GANs). Novel contributions include two anti-forensic techniques with which to hinder color interpolation identification in paper [34], an anti-forensic scheme to evade USM (unsharpmask) sharpening detection in paper [35], and a GAN-based anti-forensic method that challenges the effectiveness of CNN-based sharpening detectors in paper [36]. * Authors such as [PERSON] et al. [37; 38; 39] present techniques for the detection and classification of video tampering, while [PERSON] et al. [40; 41] focus on detecting the use of anti-forensics in video, and [PERSON] et al. [42] describe techniques for detecting video tampering using video inter-frame spoofing techniques. On the other hand, [PERSON] et al. [43; 44] propose an anti-forensic technique with which to hide the camera used to record the video. [PERSON] et al. [45] propose a similar technique and also use GAN. Papers [40; 42] focus on the race between forensic and anti-forensic techniques. Paper [40] proposes a theoretical model (VIF game) and techniques with which to detect the use of anti-forensics in video frame removal. Article [42] improves a frame removal detection algorithm and proposes an anti-anti-forensic method to deal with the manipulation of this fingerprint. Articles [41; 43; 44; 45] focus on source camera identification. Articles [41; 42] analyze the fingerprints left by video compression (H.264/AVC) to detect tampering. Articles [44; 45] address anti-forensics in source camera identification. Article [44] proposes to detect whether the camera model has been spoofed by analyzing local pixel ratios. Paper [45] employs GANs to forge the source camera model while maintaining image quality. Novel contributions include the VIF game in paper [40] to model the interaction between forgers and forensic analysts, an anti-anti-forensic method for video frame removal in paper [42], an algorithm for detecting anti-anti-forensic forgery of the source camera model in paper [44], and a GAN-based anti-anti-forensic attack to forge the source camera model with high fidelity in paper [45]. * [PERSON] et al. [46] discusses anti-forensic techniques applied to the deletion of frames in digital videos. The main novel contribution of this paper is the introduction of a joint global and local feature to detect frame dropping in video footage via the anti-forensic technique of frame interpolation. This feature overcomes the limitations of previous methods by being robust to weak residuals in HEVC videos and enabling real-time detection. * [PERSON] et al. [47; 48; 49; 50; 51; 53] propose anti-forensic tools and methods by which to bypass \"DeepFake\" detection on videos. On the other hand, [PERSON], [PERSON] et al. [52; 54] apply it to \"DeepFake\" detection in GAN-generated images. Studies [47; 48; 49; 50; 51; 52; 53; 54] propose different strategies and models for generating DeepFakes that can evade forensic detection. Paper [47] propose GAN models with additional features and loss functions designed to improve visual quality and model efficiency. Article [48] employs local perturbations to expose the vulnerability of forensic detectors, while article [49] uses a bidirectional conversion between computer-generated and natural face images. Articles [50; 51] focus on adversarial attacks for DeepFake detectors, reducing detection accuracy and highlighting the need to consider visual perception in the generation of these attacks.