# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
[129] focus their analysis on audio resampling and recompression using GAN. [PERSON] et al. [130] describe a system for classifying whether an audio recording was made in an indoor or outdoor environment and anti-forensic techniques with which to deceive the classification system. Authors such as [PERSON] et al. [131] apply anti-forensic techniques using GAN to try to misidentify the source of the recording. [PERSON] et al. [132] report anti-forensic attacks on the electrical network frequency (ENF) signal, which determines the creation date of digital audio and video recordings. Papers [127; 130] focus on the environmental signature of recordings, with paper [127] proposing an anti-forensic attack based on reverberation removal, and paper [130] presenting classifiers with which to distinguish indoor and outdoor recordings. On the other hand, papers [128; 129] address compression history manipulation. Article [128] describes an anti-forensic method of removing traces of double MP3 compression, while article [129] focuses on the creation of fake stereo audio from mono audio, avoiding the detection of this manipulation. Papers [131; 132] are notable for their use of generative adversarial neural networks (GANs); article [131] uses them to falsify the source of an audio, while article [132] analyzes their application in the anti-forensic application of the ENF signal, used to date recordings. As a novelty, paper [127] introduces machine-learning-based countermeasures to detect anti-forensic attacks on the environmental signature. Articles [129; 131] use novel techniques based on GANs for audio anti-forensic techniques. And paper [132] discusses the ENF signal anti-forensic technique, a novel approach for dating recordings. [PERSON] et al. [133] propose an algorithm based on reversible crypto-steganography, which hides sensitive information inside multimedia files such as images or videos. Also, [PERSON] et al. [134; 135] obtain a steganography algorithm applied to videos, while [PERSON] et al. [136; 137] develop a steganography algorithm for images. In contrast, [PERSON] et al. [138] describe a method by which to remove steganography in images and videos, without knowing the algorithm used for this purpose. [PERSON] et al. [139] develop an anti-forensic steganography system using the proposed multi-bit MER, which employs a flexible bit location embedding method (multi-bit MER-FBL embedding method), to overcome the problem of forensics and to achieve strong performance, including both large embedding capacity and high image quality. Articles [133; 134] propose techniques for video steganography that address security and authentication, while article [135] combines image and audio steganography in video, using forensic techniques for authentication. In the field of image steganography, papers [136; 137] introduce anti-forensic methods for images based on HoEMD and AdEMD, and multi-bit MER with flexible location, respectively, increasing capacity and making detection more difficult. Paper [139] analyzes the properties of multi-bit minimum replacement error (MER) in image steganography. On the other hand, paper [138] proposes a generic anti-forensic method by which to remove hidden information in images without knowledge of the steganographic algorithm. As novelties, articles [133; 134] introduce techniques for video steganography that address security and authentication. Articles [136; 137; 139] contribute to the development of anti-forensic methods for image steganography. Article [138] proposes a generic anti-forensic method of removing information hidden in images.