The rise of generative artificial intelligence (GenAI) has made it increasingly possible to use Deepfakes technology to generate fake pictures and videos. While this technology has benefits, it also has downsides such as spreading misinformation and endangering public interests. To address this issue, researchers have proposed various deep forgery detection algorithms and have achieved remarkable results. However, a common problem regarding these detection methods is that while in-library detection can usually achieve high accuracy, their performance is significantly degraded in cross-library detection. This indicates a severe problem of insufficient generalisation ability.To better compare the performance differences between various detection methods, this paper analyses the detection performance of the six established models of Two-stream, MesoNet, HeadPose, FWA, VA, and Multi-task. To ensure consistency, we employ a uniform evaluation framework as a benchmark for comparison. We conduct extensive intra-library and cross-library tests to evaluate these methods’ generalisation ability by utilising accuracy and error rate as key evaluation criteria for our experiments. Additionally, we further explore areas for improvement by analysing the impact of data augmentation, dataset partitioning, and threshold selection on the performance of these detection methods. Our comparative experiments are conducted on three existing fake face video datasets, including FaceForensics++, DeepfakeTIMIT, and Celeb-DF.Our research findings indicate the database partitioning method has a direct impact on the detector’s performance, and to enhance generalisation performance, the database should be divided person-based manually. The effectiveness of data augmentation techniques in improving cross-library performance is generally limited, and setting the threshold directly using source domain data often leads to a high error rate in the target domain. The findings of this paper provide insights into the development of more effective detection methods to combat the harmful effects of Deepfakes.

Child sexual abuse is a serious global problem that has gained public attention in recent years. Due to the popularity of digital cameras, many perpetrators take images of their sexual activities. Traditionally, it has been difficult to use vein patterns in evidence images for forensic identification, because they were nearly invisible in colour images. State-of-the-art techniques, and computational methods including optical-based vein uncovering or artificial neural networks have recently been introduced to extract vein patterns for identification purposes. However, these methods are still not mature due to limitations such as lack of reliable feature extraction, efficient uncovering algorithms, and matching difficulties. In this paper, we propose two new schemes to overcome some of these limitations by using sparse auto-encoder and adaptive contrast enhancement. Specifically, an adjustment sparse auto-encoder parameters scheme is used for optimising parameters, and then optimised parameters are automatically trained to enhance the robustness of vein visualisation and feature extraction. We also use a pair of synchronised colour and near infrared NIR images to generate the skeletonised vein patterns for verifying the outcome of the proposed method. The proposed algorithm was examined on a database with 100 pairs of colour and NIR images collected from different parts of the body such as forearms, thighs, chests and ankles. The experimental results are encouraging and indicate that the proposed method improves the feature extraction procedure, which can lead to better uncovering results compared with current methods.

Authentication methods based on some human traits, including fingerprint, face, iris, and palmprint, have been developed significantly, and currently, they are mature enough which have been reliably considered for person identification purposes. Recently, as a new research area, few methods based on non-facial skin features such as vein patterns have been developed. This extended abstract briefly explores some key features of biometric traits whereas vein pattern recognition is also outlined.

Malware is an international software disease. Research shows that the effect of malware is becoming chronic. To protect against malware detectors are fundamental to the industry. The effectiveness of such detectors depends on the technology used. Therefore, it is paramount that the advantages and disadvantages of each type of technology are scrutinized analytically. This study's aim is to scrutinize existing publications on this subject and to follow the trend that has taken place in the advancement and development with reference to the amount of information and sources of such literature. Many of the malware programs are huge and complicated and it is not easy to comprehend the details. Dissemination of malware information among users of the Internet and also training them to correctly use anti-malware products are crucial to protecting users from the malware onslaught. This paper will provide an exhaustive bibliography of methods to assist in combating malware.

This paper recasts the problem of online secondary path modeling in the form of a statistical inverse problem. A statistical and, in particular, a Bayesian approach towards secondary path modeling is developed and the computational issues that emerge from this approach are discussed. All signals and parameters are modeled as random variables and the degree of information concerning them is coded in their probability density functions. An abstract solution is formulated in the form of a probability density function for the secondary path model. For extracting point estimates, common statistical estimation methods are investigated. It is shown that maximum likelihood estimation is not stable; however, Bayesian method of maximum a posteriori gives a reliable solution. An adaptive algorithm is then developed to compute this solution in a computationally efficient manner. This algorithm has three advantages, compared to the traditional secondary path modeling algorithms. First, it does not cause any interference with the main active noise control algorithm. Second, it does not require any additive-noise to be injected into the secondary path. Third, it does not require any off-line initiation. The convergence of the proposed algorithm is analyzed theoretically. The validity of the theoretical results is investigated by using computer simulation. Finally successful integration of the proposed algorithm into a real-time ANC system is reported.

This paper extends the existing work on the root locus analysis of FxLMS algorithm by considering secondary path modeling errors. Rules for sketching FxLMS root locus are set out. An analytic convergence condition is then derived from the root locus plot. A deliberately-misaligned secondary path model is proposed to be used as the data preparation filter in the FxLMS algorithm. The proposed filter increases the computational efficiency of the algorithm, without changing its convergence behavior. The theoretical results are verified in practice by busing an experimental system.

This paper concerns about the theoretical stability of the adaptation process performed by the Filtered-x Least Mean Square (FxLMS) algorithm in active control of acoustic noise. A dynamic model for the Variance of Residual Acoustic Noise (VRAN) is developed and it is shown that the stability of this model is a sufficient condition for the stability of the adaptation process. The basic rules governing the VRAN root locus are developed, based on which an upper-bound for the adaptation step-size is derived. This upper-bound can apply to a general case with an arbitrary secondary path, unlike the traditional upper-bound used in adaptive filter theory, which was derived only for pure delay secondary paths.

This study represents a stochastic model for the adaptation process performed on adaptive control systems by the filtered‐x least‐mean‐square (FxLMS) algorithm. The main distinction of this model is that it is derived without using conventional simplifying assumptions regarding the physical plant to be controlled. This model is then used to derive a set of closed‐form mathematical expressions for formulating steady‐state performance, stability condition and learning rate of the FxLMS adaptation process. These expressions are the most general expressions, which have been proposed so far. It is shown that some previously derived expressions can be obtained from the proposed expressions as special and simplified cases. In addition to computer simulations, different experiments with a real‐time control setup confirm the validity of the theoretical findings.