Cybersickness, triggered by prolonged virtual exposure, poses a major challenge in virtual reality and human-computer interaction. Traditional prediction methods rely on subjective questionnaires, limited by bias and delayed response. To address these issues, we propose CyberGATE, a multimodal cybersickness prediction framework based on adaptive multiscale gated fusion of eye and head movement data. CyberGATE integrates a global pathway, which combines Multi-View Embedding, Mamba modeling, and KAN-based Mixture of Experts for long-range temporal modeling, and a local pathway employing patch transformers and local attention for short-term fluctuation analysis. Experiments on two public datasets show that Cyber-GATE outperforms existing approaches, offering a more reliable solution for cybersickness prediction in VR environments.

The rapid evolution of deep generative models poses a critical challenge to deepfake detection, as detectors trained on forgery-specific artifacts often suffer significant performance degradation when encountering unseen forgeries. While existing methods predominantly rely on spatial domain analysis, frequency domain operations are primarily limited to feature-level augmentation, leaving frequency-native artifacts and spatial-frequency interactions insufficiently exploited. To address this limitation, we propose a novel detection framework that integrates multiscale spatial-frequency analysis for universal deepfake detection. Our framework comprises three key components: (1) a local frequency feature extraction branch that combines block-wise discrete cosine transform with cascaded multi-scale convolutions to capture subtle spectral artifacts; (2) a global frequency feature extraction branch utilizing scale-invariant derivative accumulation to identify holistic forgery distribution patterns; and (3) a multi-stage cross-modal fusion mechanism that incorporates shallow-layer attention enhancement and deep-layer dynamic modulation to model spatial-frequency interactions. Extensive evaluations on widely adopted benchmarks demonstrate that our method outperforms state-of-the-art deepfake detection methods in both accuracy and generalizability.

Summary The topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resources. A combination weighted clustering algorithm is proposed in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. First, a clustering algorithm considering the available channel, geographic location, and experienced data (used for collecting the behavior of secondary users (SUs) in the network and the evaluation on it) of SUs is put forward through analyzing the characteristics of the idle channels in cognitive network. Three factors, namely, average channel capacity, stability, and channel quality, are converted into quantifiable values. The cluster head is determined on the basis of the three factors. Then, the cluster members and gateway nodes are determined using the weighting formula and the location information of the cluster head. Results show that the proposed clustering algorithm can generate 15% more clusters than other algorithms and reduce 40% of network communication overhead when the transmission distance between cognitive users and the channel number change. Thus, the stability of the cluster structure is maintained and the communication overhead is decreased. This study provides references for the construction of the stable distributed cognitive network.

Inpainting, originally designed in computer vision to reconstruct lost or deteriorated parts of images and videos, has been used for image tampering, including region filling and object removal to alter the truth. While several types of tampering including copy-move and seam carving forgery can now be successfully exposed in image forensics, there has been very little study to tackle inpainting forgery in JPEG images, the detection of which is extremely challenging due to the post-recompression attacks performed to cover or compromise original inpainting traces. To date, there is no effective way to detect inpainting image forgery under combined recompression attacks. To fill such a gap in image forensics and reveal inpainting forgery from the post-recompression attacks in JPEG images, we propose in this paper an approach that begins with large feature mining in discrete transform domain, ensemble learning is then applied to deal with the high feature dimensionality and to prevent the overfitting that generally happens to some regular classifiers under high feature dimensions. Our study shows the proposed approach effectively exposes inpainting forgery under post recompression attacks; especially, it noticeably improves the detection accuracy while the recompression quality is lower than the original JPEG image quality, and thus bridges a gap in image forgery detection.

With the booming of smart phone and high-speed wireless networks in recent years, applications and data have been shifting from desktop to mobile devices at a vigorous pace. Although mobile computing provides great convenience in daily life, it becomes vulnerable to various types of emerging attacks. User authentication plays an indispensable role in protecting computer systems and applications, but the development of touch screen hardware and user habit change post requirements for new authentication methods for mobile and tablets devices. In this paper, we present a robust user authentication scheme using both static and dynamic features of touch gestures. We take advantage of the pressure sensitivity of multi-touch screens to obtain irreproducible biometric patterns. Discriminative features such as distance, angle, and pressure are extracted from the touch-point data, and used in statistical analysis for verification. We tested our scheme in a variety of experiments that involved multiple volunteers to perform various gestures. The analysis of experimental results and user feedback indicate the proposed scheme delivers comprehensive measurements and accurate pattern classification for touch gestures. Based on these results, we conclude that the proposed scheme overcomes the limitations of the existing user authentication methods, and shows great potential to provide robust protection against unauthorized access.

The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.

This article presents a second-order derivative-based audio steganalysis. First, Mel-cepstrum coefficients and Markov transition features from the second-order derivative of the audio signal are extracted; a support vector machine is then applied to the features for discovering the existence of hidden data in digital audio streams. Also, the relation between audio signal complexity and steganography detection accuracy, which is an issue relevant to audio steganalysis performance evaluation but so far has not been explored, is analyzed experimentally. Results demonstrate that, in comparison with a recently proposed signal stream-based Mel-cepstrum method, the second-order derivative-based audio steganalysis method gains a considerable advantage under all categories of signal complexity--especially for audio streams with high signal complexity, which are generally the most challenging for steganalysis-and thereby significantly improves the state of the art in audio steganalysis.

With the booming trend of cloud computing, on-demand resource management is overwhelming static and dedicated strategy. The increasing demands introduce multiple challenges including energy efficiency, performance enhancement, and fault tolerance. Virtualized computing environment decouples OS and applications with hardware to best facilitate these on-demand cloud services. In this paper, we propose an online learning approach for resource auto-configuration of distributed virtual machines to support multilayer web applications. Based on performance metrics from host OS, virtual machine, and application server, the approach is able to adjust resource configuration and direct virtual machine migration corresponding to service demand variations. Support vector regression is applied to control reconfiguration and migration. The approach will be evaluated by using TPC-E benchmark on multi-layer web applications deployed on networked virtual machines. Our approach will guide systems with proactive changes to improve dependability, efficiency, and reduce the power consumption.

MP3 is the most popular format for audio storage and a de facto standard of digital audio compression for the transfer and playback. The flexibility of compression ratio of MP3 coding enables users to choose their customized configuration in the trade-off between file size and quality. Double MP3 compression often occurs in audio forgery, steganography and quality faking by transcoding an MP3 audio to a different compression ratio. To detect double MP3 compression, in this paper, we extract the statistical features on the modified discrete cosine transform, and apply support vector machines and a dynamic evolving neuron-fuzzy inference system to the extracted features for classification. Experimental results show that our method effectively and accurately detects double MP3 compression for both up-transcoded and down-transcoded MP3 files. Our study also indicates the potential for mining the audio processing history for forensic purposes.

In this article, we propose a scheme for detecting hidden messages in compressed audio files produced by MP3Stego, as our literature search has found no previous work on successful steganalysis of MP3Stego. We extract moment statistical features on the second derivatives, as well as Markov transition features and neighboring joint density of the MDCT coefficients based on each specific frequency band on MPEG-1 audio layer 3. A support vector machine is applied to different feature sets for classification. Experimental results show that our approach is successful to discriminate MP3 covers and the steganograms generated by using MP3Stego.

In this paper, we present a novel stream data mining for audio steganalysis, based on second order derivative of audio streams. We extract Mel-cepstrum coefficients and Markov transition features on the second order derivative, a support vector machine is applied to the features for discovery of the existence of covert message in digital audios. We also explore the relation between signal complexity and detection performance on digital audios, which has not been studied previously. Our study shows that, in comparison with a recently proposed signal stream based Mel-cepstrum steganalysis, our method prominently improves the detection performance, which is not only related to information-hiding ratio but also signal complexity. Generally speaking, signal stream based Mel-cepstrum audio steganalysis performs well in steganalysis of audios with low signal complexity; it does not work so well on audios with high signal complexity. Our stream mining approach for audio steganalysis gains significant advantage in each category of signal complexity - especially in audios with high signal complexity, and thus improves the state of the art in audio steganalysis.

Detection of information-hiding in JPEG images is actively delivered in steganalysis community due to the fact that JPEG is a widely used compression standard and several steganographic systems have been designed for covert communication in JPEG images. In this paper, we propose a novel method of JPEG steganalysis. Based on an observation of bi-variate generalized Gaussian distribution in Discrete Cosine Transform (DCT) domain, neighboring joint density features on both intra-block and inter-block are extracted. Support Vector Machines (SVMs) are applied for detection. Experimental results indicate that this new method prominently improves a current art of steganalysis in detecting several steganographic systems in JPEG images. Our study also shows that it is more accurate to evaluate the detection performance in terms of both image complexity and information hiding ratio.

To improve a recently developed mel-cepstrum audio steganalysis method, we present in this paper a method based on Fourier spectrum statistics and mel-cepstrum coefficients, derived from the second-order derivative of the audio signal. Specifically, the statistics of the high-frequency spectrum and the mel-cepstrum coefficients of the second-order derivative are extracted for use in detecting audio steganography. We also design a wavelet-based spectrum and mel-cepstrum audio steganalysis. By applying support vector machines to these features, unadulterated carrier signals (without hidden data) and the steganograms (carrying covert data) are successfully discriminated. Experimental results show that proposed derivative-based and wavelet-based approaches remarkably improve the detection accuracy. Between the two new methods, the derivative-based approach generally delivers a better performance.

Mass spectrometry becomes the most widely used measurement in proteomics research. High dimensionality of features and small dataset are two major limitations restrict the accuracy of classification in mass spectrum data analysis. To improve the data mining result, two major issues need to be highlighted, which are feature extraction and feature selection. The quality of the feature set determines the reliability of the prediction of disease status. A well-known approach is to detect peak values and apply support vector machine recursive feature elimination (SVMRFE) to choose feature sets for classification. In this article, we successfully apply a distance metric learning to classify proteomics mass spectrometry data. Experimental results show that distance metric learning can successfully be applied to the classification of proteomics data and the results are comparable to the best results by applying SVM to the feature sets chosen with the use of SVMRFE.

In this article, we propose a scheme of detecting the information-hiding in videos based on the pairs of condition and joint distributions in the transform domains. Specifically, based on the approach of the Markov-process in JPEG image steganalysis and our previous work, we propose the pairs of condition and joint distribution of the neighbor difference in the transform domains, including discrete cosine transform (DCT) and the discrete wavelet transform (DWT). We apply learning classifiers to the pairs extracted from the video covers and the video steganograms produced by MSU Video Steganograms. Experimental results show that this approach is very successful in detecting the information-hiding in MSU stego video steganograms.

In this article, we propose a steganalysis method for detecting the presence of information-hiding behavior in wav audios. We extract the neighboring joint distribution features and the Markov features of the second order derivative, and combine these features with the error response by randomly modifying the least significant bit, then apply learning machines to the features for distinguishing the stegoaudios from cover videos. Experimental results show that our method performs well in steganalysis of the audio stegograms that are produced by using Hide4PGP, Invisible Secrets and S-tools4.