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An acoustic sea glider has been developed for ambient sea noise measurement and target detection through the deployment of an acoustic vector sensor (AVS). The glider was designed with three cabins connected in sequence and it can dive to depths exceeding 1200m. The AVS fixed on the glider measure acoustic pressure and particle velocities related to undersea noise, and the inner attitude sensors can effectively eliminate the estimation deviation of the direction of arrival. The inherent self-noises of the acoustic sea glider and AVS are presented respectively in respect to the Knudsen spectra of sea noise. Sea trial results indicate that the AVS could work well for undersea noise measurement when the glider is smooth sliding, and the target azimuth estimated by AVS after correction is remarkably consistent with the values measured by the GPS, and direction-finding errors are less than 10 degrees. The research in this paper shows that the acoustic sea glider is able to undertake tasks such as a wide range of underwater acoustic measurement and detection.

Source camera identification can verify whether two videos were shot by the same device, which is of great significance in multimedia forensics. Most existing identification methods use convolutional neural networks to learn sensor noise patterns to identify the source camera in closed forensic scenarios. While these methodologies have achieved remarkable results, they are nonetheless constrained by two primary challenges: (1) the interference of semantic information and (2) the incongruity in feature distributions across different datasets. The former will interfere with the extraction of effective features of the model. The latter will cause the model to fit the characteristic distribution of the training data and be sensitive to unseen data features. To address these challenges, we propose a novel source camera identification framework that determines whether a video was shot by the same device by obtaining similarities between source camera features. Firstly, we extract video key frames and use the integral image to optimize the smoothing blocks selection algorithm of inter-pixel variance to remove the interference of video semantic information. Secondly, we design a residual neural network fused with a constraint layer to adaptively learn video source features. Thirdly, we introduce a triplet loss metric learning strategy to optimize the network model to improve the discriminability of the model. Finally, we design a multi-dimensional feature vector similarity fusion strategy to achieve highly generalized source camera recognition. Extensive experiments show that our method achieved an AUC value of up to 0.9714 in closed-set forensic scenarios and an AUC value of 0.882 in open-set scenarios, representing an improvement of 5% compared to the best baseline method. Furthermore, our method demonstrates effectiveness in the task of deepfake detection. [ABSTRACT FROM AUTHOR]

Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on probability distribution. Specifically, we propose a stacked ensemble learning-based detection model for anomalous traffic (or SEDAT for short) to defend against highly concealed multiscale attacks. The model employs a random forest (RF)-based method to select features and introduces multiple base learning autoencoders (AEs) to enhance the representation of multiscale attack behaviors. In addressing the challenge of a single model's inability to capture the regularities of multiscale attack behaviors, SEDAT is capable of adapting to the complex multiscale characteristics in network traffic, enabling the prediction of network access behavior. Comparative experiments demonstrate that SEDAT exhibits superior detection capabilities in multiscale network attacks. In particular, SEDAT achieves an improvement of at least 5% accuracy over baseline methods for detecting multiscale attacks. [ABSTRACT FROM AUTHOR]

The existence of adversarial image makes us have to doubt the credibility of artificial intelligence system. Attackers can use carefully processed adversarial images to carry out a variety of attacks. Inspired by the theory of image compressed sensing, this paper proposes a new black-box attack, \(\mathcal {N}\text{-HSA}_{LF}\). It uses covariance matrix adaptive evolution strategy (CMA-ES) to learn the distribution of adversarial perturbation in low frequency domain, reducing the dimensionality of solution space. And sep-CMA-ES is used to set the covariance matrix as a diagonal matrix, which further reduces the dimensions that need to be updated for the covariance matrix of multivariate Gaussian distribution learned in attacks, thereby reducing the computational cost of attack. And on this basis, we propose history-driven mean update and current optimal solution-guided improvement strategies to avoid the evolution of distribution to a worse direction. The experimental results show that the proposed \(\mathcal {N}\text{-HSA}_{LF}\) can achieve a higher attack success rate with fewer queries on attacking both CNN-based and transformer-based target models under \(L_2\) -norm and \(L_\infty\) -norm constraints of perturbation. We also conduct an ablation study and the results show that the proposed improved strategies can effectively reduce the number of visits to the target model when making adversarial examples for hard examples. In addition, our attack is able to make the integrated defense strategy of GRIP-GAN and noise-embedded training ineffective to a certain extent. [ABSTRACT FROM AUTHOR]

In recent years, analysis dictionary learning (ADL) model has attracted much attention from researchers, owing to its scalability and efficiency in representation-based classification. Despite the supervised label information embedding, the classification performance of analysis representation suffers from the redundant and noisy samples in real-world datasets. In this paper, we propose a joint Dual-Structural constrained and Non-negative Analysis Representation (DSNAR) learning model. First, the supervised latent structural transformation term is considered implicitly to generate a roughly block diagonal representation for intra-class samples. However, this discriminative structure is fragile and weak in the presence of noisy and redundant samples. To highlight both intra-class similarity and inter-class separation for class-oriented representation, we then explicitly incorporate an off-block suppressing term on the ADL model, together with a non-negative representation constraint, to achieve a well-structured and meaningful interpretation of the contributions from all class-oriented atoms. Moreover, a robust classification scheme in latent space is proposed to avoid accidental incorrect predictions with noisy information. Finally, the DSNAR model is alternatively solved by the K-SVD method, iterative re-weighted method and gradient method efficiently. Extensive classification results on five benchmark datasets validate the performance superiority of our DSNAR model compared to other state-of-the-art DL models. [ABSTRACT FROM AUTHOR]

High-dimensional data samples tend to contain highly correlated features and are quite fragile to various noises and outliers in practical applications. For subspace clustering models, it has appeared to be inadequate to adopt conventional norm-based distance measurements to resist feature contaminations by undetermined types of noises. As a consequence, the learned low-dimensional representation is not always reliable and discriminative, which inevitably impedes the clustering performance. To remedy the deficiencies, we propose a robust subspace clustering model via auto-weighted and dual-structural representation (AWDSR) learning. Specifically, a feature-weighted reconstruction term is first introduced to the self-representation framework to automatically reinforce important features by measuring the self-representational reconstruction loss. As such, various types of noise features in data samples could be adaptively assigned with relatively small weights to reduce the residual elements of the reconstruction term. Moreover, an adaptive dual-structural constraint is simultaneously considered to guarantee discriminative block-diagonal representation. Then, an efficient alternative optimization method with guaranteed convergence and relatively low complexity is developed to optimize the challenging objective function. Finally, we carry out extensive experiments to compare the AWDSR model with other state of the arts on one synthetic data and six real-world databases. Experimental results fully demonstrate the effectiveness and superiority of the proposed approach in terms of accuracy and normalized mutual information metrics. [ABSTRACT FROM AUTHOR]