CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis.Comment: Submitted to IEEE Transactions on Information Forensics and Securit

Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks

No-Reference Video Quality Assessment (NR-VQA) plays an essential role in improving the viewing experience of end-users. Driven by deep learning, recent NR-VQA models based on Convolutional Neural Networks (CNNs) and Transformers have achieved outstanding performance. To build a reliable and practical assessment system, it is of great necessity to evaluate their robustness. However, such issue has received little attention in the academic community. In this paper, we make the first attempt to evaluate the robustness of NR-VQA models against adversarial attacks under black-box setting, and propose a patch-based random search method for black-box attack. Specifically, considering both the attack effect on quality score and the visual quality of adversarial video, the attack problem is formulated as misleading the estimated quality score under the constraint of just-noticeable difference (JND). Built upon such formulation, a novel loss function called Score-Reversed Boundary Loss is designed to push the adversarial video's estimated quality score far away from its ground-truth score towards a specific boundary, and the JND constraint is modeled as a strict $L_2$ and $L_\infty$ norm restriction. By this means, both white-box and black-box attacks can be launched in an effective and imperceptible manner. The source code is available at https://github.com/GZHU-DVL/AttackVQA

HVS Revisited: A Comprehensive Video Quality Assessment Framework

Video quality is a primary concern for video service providers. In recent years, the techniques of video quality assessment (VQA) based on deep convolutional neural networks (CNNs) have been developed rapidly. Although existing works attempt to introduce the knowledge of the human visual system (HVS) into VQA, there still exhibit limitations that prevent the full exploitation of HVS, including an incomplete model by few characteristics and insufficient connections among these characteristics. To overcome these limitations, this paper revisits HVS with five representative characteristics, and further reorganizes their connections. Based on the revisited HVS, a no-reference VQA framework called HVS-5M (NRVQA framework with five modules simulating HVS with five characteristics) is proposed. It works in a domain-fusion design paradigm with advanced network structures. On the side of the spatial domain, the visual saliency module applies SAMNet to obtain a saliency map. And then, the content-dependency and the edge masking modules respectively utilize ConvNeXt to extract the spatial features, which have been attentively weighted by the saliency map for the purpose of highlighting those regions that human beings may be interested in. On the other side of the temporal domain, to supplement the static spatial features, the motion perception module utilizes SlowFast to obtain the dynamic temporal features. Besides, the temporal hysteresis module applies TempHyst to simulate the memory mechanism of human beings, and comprehensively evaluates the quality score according to the fusion features from the spatial and temporal domains. Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA methods. Ablation studies are further conducted to verify the effectiveness of each module towards the proposed framework.Comment: 13 pages, 5 figures, Journal pape

A novel dilated contextual attention module for breast cancer mitosis cell detection

Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity.Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells.Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model’s ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step.Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model’s performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage.Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers

Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination

Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement.Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method’s performance is further evaluated.Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm.Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples

Frequent alterations in cytoskeleton remodelling genes in primary and metastatic lung adenocarcinomas

The landscape of genetic alterations in lung adenocarcinoma derived from Asian patients is largely uncharacterized. Here we present an integrated genomic and transcriptomic analysis of 335 primary lung adenocarcinomas and 35 corresponding lymph node metastases from Chinese patients. Altogether 13 significantly mutated genes are identified, including the most commonly mutated gene TP53 and novel mutation targets such as RHPN2, GLI3 and MRC2. TP53 mutations are furthermore significantly enriched in tumours from patients harbouring metastases. Genes regulating cytoskeleton remodelling processes are also frequently altered, especially in metastatic samples, of which the high expression level of IQGAP3 is identified as a marker for poor prognosis. Our study represents the first large-scale sequencing effort on lung adenocarcinoma in Asian patients and provides a comprehensive mutational landscape for both primary and metastatic tumours. This may thus form a basis for personalized medical care and shed light on the molecular pathogenesis of metastatic lung adenocarcinoma

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Anti‐forensics for double JPEG compression based on deep reinforcement learning

Abstract DJPEG (double JPEG compression) detection has received attention in image forensics. In order to study the limitations of forensics detectors under attacks, it is essential to develop DJPEG anti‐forensics techniques. Compared with anti‐forensics operations performed in spatial domain, existing DCT‐based methods have not brought their potential to full play due to the difficulty of directly manipulating DCT coefficients. In recent years, DRL (deep reinforcement learning) has developed rapidly in image‐related tasks. Its ability of decision making can turn an unpredictable restoration step into multiple simple trial‐and‐error modification steps. In this paper, it is investigated how to efficiently modify DCT coefficients for anti‐forensics purpose with the help of DRL, and propose a method called AFDJ‐DRL (Anti‐forensics Framework for DJPEG based on DRL). Specifically, an agent utilizes a policy network to extract DCT inter‐block and intra‐block features and learn a coefficient‐level policy. An environment assigns rewards from multiple sources. Via maximizing such rewards, the agent can learn to modify DCT coefficients in several rounds to obtain images with anti‐forensics capability. Experimental results show that the AFDJ‐DRL is superior to existing DCT‐based methods, and can be applied as a post‐processing step for spatial ones for further performance improvement

MODELING DYNAMIC CORRELATIONS AND SPILLOVER EFFECTS OF COUNTRY RISK: EVIDENCE FROM RUSSIA AND KAZAKHSTAN

Oil economies in the Former Soviet Union (FSU) region, with geographical proximity to each other, are usually impacted by some common risk factors, which make their country risks closely correlated. This paper focuses on correlation between country risks and investigates the spillovers of country risk returns (CRR). Taking Russia and Kazakhstan for example, firstly, this paper identifies the structural breaks in CRR series, using iterated cumulative sums of squares (ICSS) algorithm. Secondly, on the assumption that there may be similarity in structural breaks of CRR series of the two countries, Vector Autoregression (VAR) process and Granger causality test are used to identify whether there are mean spillovers of CRR series. Finally, the volatility spillovers are captured by using multivariate conditional volatility models in the framework of the BEKK models. Empirical results show that (1) there are significant unidirectional mean spillovers from Russia to Kazakhstan; (2) there are asymmetric bidirectional volatility spillovers between Russia and Kazakhstan; and volatility spillover effects from Russia to Kazakhstan are stronger.Country risk, oil economy, dynamic correlation, structural break, spillovers

Protective Effect of <i>Citrus Medica limonum</i> Essential Oil against <i>Escherichia coli</i> K99-Induced Intestinal Barrier Injury in Mice

Citrus Medica limonum essential oil (LEO) has been reported to have antibacterial and anti-inflammatory activities, but its protective effect in the intestine remains unknown. In this study, we researched the protective effects of LEO in relation to intestinal inflammation induced by E. coli K99. The mice were pretreated with 300, 600, and 1200 mg/kg LEO and then stimulated with E. coli K99. The results showed that E. coli K99 caused immune organ responses, intestinal tissue injury, and inflammation. LEO pretreatment dose-dependently alleviated these changes by maintaining a low index in the thymus and spleen and producing a high content of immunoglobulin A, G, and M (IgA, IgG, and IgM) and low content of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Intestinal integrity as a consequence of the LEO pretreatment may be related to the high mRNA expression of intestinal trefoil factor (ITF) and the low mRNA expression of transforming growth factor-β1 (TGF-β1). Conclusively, an LEO pretreatment can alleviate E. coli K99-induced diarrhea, immune organ response, and body inflammation in mice by reducing the levels of inflammatory cytokines and improving the levels of immunoglobulin, and the intestinal integrity remained highest when maintaining the high mRNA expression of ITF and keeping the mRNA expression of TGF-β1 low in the intestinal tissue