Audio splicing forgery involves cutting specific parts of an audio recording and inserting or combining them into another audio recording. This manipulation technique is often used to create misleading or fake audio content, particularly in digital media environments. The detection of audio splicing forgery is of great importance, especially in forensic analysis, security applications and media verification processes. In this paper, we present a novel noise robust method for detecting audio splicing forgery. The proposed method converts audio signals into cochleagram images, which are then input into SWIN transformer model for training. Following the training process, the model classifies and labels test audio files as either original or fake. In the experiments, the method is tested on data sets of varying durations. The results demonstrate high performance across different datasets, both without and with Gaussian noise, as well as under real-world environmental noise attacks with varying audio durations. For example, under 30 dB noise condition on 2-second data segments, the model achieved an accuracy of 94.33%, precision of 96.46%, recall of 92.90%, and an F1-score of 94.65%. For rain noise condition, the proposed method achieves the highest accuracy of 93.26%, precision of 99.83%, and F1-score of 95.48% .

Nowadays, production and distribution of digital images has become part of our life. Since digital images, which are important carriers of information, are considered as the concrete proofs of facts in many fields and they can be used as evidence in the courts of law, development of techniques to ensure image authenticity is an active research topic. Copy-move forgery is one of the most common manipulation techniques that are implemented on the digital images, and various techniques have been developed for detection of these kinds of forgeries. JPEG format, which presents the ability of making high rate compression without causing remarkable changes in the meaning of the image, is the most commonly used format on digital images. In this study, the topic of detecting free-form copy-move forgeries on digital images is covered. It has been observed that the developed technique is able to detect the professional forgeries in which the copied region is selected in free-form and which are almost impossible to be detected by human eye, with high success rate, and it is able to give successful results even if the image is exposed to postprocesses such as JPEG compression and Gaussian filtering, which make the detection of forgery harder.

Multimedia devices have become increasingly popular due to high quality and low cost products using advanced technology. These devices can capture multimedia files, which can be modified easily by video editing tools. One of the most frequently encountered forgery types in video forensics is the frame duplication (FD) forgery. Many methods have been proposed in the literature to deal with this type of forgery. These methods do not consider frame‐mirroring (FM) attack which copy a sequence of frames and paste its mirrored versions somewhere else on the same video. A new FD/FM detection method is proposed in this work. The method extracts binary features from frames and determines the similarity among features. Peak‐signal‐to‐noise ratio of the candidate frames is used to eliminate some of the large number of candidates to improve the detection of the forged frames. Experimental results show that the proposed method successfully detects FM/FD attacks and also yields better execution time and detection results compared to similar works reported in the literature.