Robust blind watermarking enables the accurate recovery of embedded watermarking without requiring access to the original images or message, and is widely applied in copyright protection and traceability tracking. However, existing flow-based robust blind watermarking methods typically rely on auxiliary variables during the extraction process that are inconsistent with the property of the information lost during embedding, and are often tailored to a single known noise type, resulting in limited robustness against unknown noise. To address these limitations, we propose a flow-based watermarking method that enhances robustness to unknown noise through feature preservation and gradient perturbation. Specifically, we propose a feature preservation network that learns auxiliary variables from noisy images, aligning them with the property of the lost information to improve the robustness of watermarking extraction. In addition, we design an unknown noise image generation method based on gradient perturbation, which iteratively adds perturbations to encoded images to simulate a variety of potential noise patterns that lead to watermarking extraction errors, thereby improving the robustness of watermarking against unknown noises. Experimental results validate the effectiveness of our method, demonstrating an improvement in watermarking extraction accuracy and a 10 dB increase in peak signal-to-noise ratio (from 33.5 dB to 43.8 dB). The source code is publicly available at https://github.com/reliarui/FPGP.

Abstract Breast cancer is one of the most common types of cancer around the world. The early‐stage recognition of breast cancer is favourable for the diagnosis and treatment of the affecting patient. Data mining approaches can ease the diagnosis of breast cancer by analysis of the associated dataset for real‐time decision‐making. The present study proposes an effective transformation approach of experimental attributes of a breast cancer dataset using the latent semantic analysis and the fusion of derived features with classification methods for accurate recognition of breast cancer. The proposed approach is validated using the most widely used benchmark and open‐access breast cancer dataset. The transformed features of the original dataset result in 100% recognition efficiency using a multilayer perceptron, support vector machine, multi‐class classifier and functional tree classifiers. Other classifiers, like naïve Bayes, rotation forest, simple linear logistic regression and logistic model tree result in recognition accuracy between 96.85% and 99.30% using a similar feature subset. Besides, the optimal subset of derived features has been affirmed based on the evaluation metrics of classification approaches.

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings.

The emergence of adversarial examples has had a significant impact on the development and application of deep learning. In this paper, a novel convolutional neural network model, the stochastic multifilter statistical network (SmsNet), is proposed for the detection of adversarial examples. A feature statistical layer is constructed to collect statistical data of feature map output from each convolutional layer in SmsNet by combining manual features with a neural network. The entire model is an end-to-end detection model, so the feature statistical layer is not independent of the network, and its output is directly transmitted to the fully connected layer by a short-cut connection called the SmsConnection. Additionally, a dynamic pruning strategy is introduced to simplify the model structure for better performance. The experiments demonstrate the effectiveness of the network structure and pruning strategy, and the proposed model achieves high detection rates against state-of-the-art adversarial attacks.

Meteorological parameters were crucial and effective factors in past infectious diseases, like influenza and severe acute respiratory syndrome (SARS), etc. The present study targets to explore the association between the coronavirus disease 2019 (COVID-19) transmission rates and meteorological parameters. For this purpose, the meteorological parameters and COVID-19 infection data from 28th March 2020 to 22nd April 2020 of different states of India have been compiled and used in the analysis. The gradient boosting model (GBM) has been implemented to explore the effect of the minimum temperature, maximum temperature, minimum humidity, and maximum humidity on the infection count of COVID-19. The optimal performance of the GBM model has been achieved after tuning its parameters. The GBM results in the best accuracy of R 2 = 0.95 for prediction of active cases in Maharashtra, and R 2 = 0.98 for prediction of recovered cases of COVID-19 in Kerala and Rajasthan, India.

Due to the popularity of JPEG format images in recent years, JPEG images will inevitably involve image editing operation. Thus, some tramped images will leave tracks of Non-aligned double JPEG ( NA-DJPEG ) compression. By detecting the presence of NA-DJPEG compression, one can verify whether a given JPEG image has been tampered with. However, only few methods can identify NA-DJPEG compressed images in the case that the primary quality factor is greater than the secondary quality factor. To address this challenging task, this article proposes a novel feature extraction scheme based optimized pixel difference ( OPD ), which is a new measure for blocking artifacts. Firstly, three color channels (RGB) of a reconstructed image generated by decompressing a given JPEG color image are mapped into spherical coordinates to calculate amplitude and two angles (azimuth and zenith). Then, 16 histograms of OPD along the horizontal and vertical directions are calculated in the amplitude and two angles, respectively. Finally, a set of features formed by arranging the bin values of these histograms is used for binary classification. Experiments demonstrate the effectiveness of the proposed method, and the results show that it significantly outperforms the existing typical methods in the mentioned task.

Abstract In the present study, the performance of physicochemical descriptors of metal oxide nanomaterials on the basis of their origins, including descriptors related to element/ion, the metal oxide in bulk, and metal oxide in the media for toxicity modelling has been evaluated. Three published experimental nanomaterial data sets were selected for the study. The data set was divided into three subsets on the basis of the origin of descriptors; thereafter, each of them was analysed using principal component analysis for visual discrimination of toxic versus nontoxic nanomaterials in the principal component (PC) space. The metal oxide in media‐based descriptor subset results in the best visual clustering of toxicity of nanomaterials compared with the rest two subsets. It was also confirmed with the class separability measures in the PC space and classification accuracy of the support vector machine (SVM) method. PC scores of the metal oxide in media‐related descriptors results in the maximum value of class separability index (J = 0.0049) and the maximum classification accuracy of 96.43% of SVM classifier (sensitivity of 100%). A toxicity classification model of nanomaterials has been established using PC scores of optimal descriptor subset and SVM method.

Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.

Abstract Healthcare data analysis is currently a challenging and crucial research issue for the development of a robust disease diagnosis and prediction system. Many specific and a few common methods have been discussed in the literature for healthcare data classification. The present study implements 32 classification methods of six categories (Bayes, function‐based, lazy, meta, rule‐based, and tree‐based) with the objective of searching the best and common categories and methods in healthcare data mining. The performance of each classification method has been evaluated based on analysis time, classification accuracy, precision, recall, F‐measure, area under the receiver operating characteristic curve, root mean square error, kappa coefficient, Kulczynski's measure, and Fowlkes–Mallows index and compared with more than 90 classification methods used in past studies. Seventeen healthcare datasets related to thyroid, cancer, skin disease, heart disease, hepatitis, lymphography, audiology, diabetes, surgery, arrhythmia, postsurvival, liver, and tumour have been used in the performance assessment of the classification methods. The tree‐based classification methods have a better performance (with an average classification accuracy of 79.92% and maximum accuracy of 99.50%; an analysis time of 3.91 s for the logistic model tree classifier) than the other methods. Furthermore, the association of datasets and classification methods has been discussed.

In this paper, a novel forensics scheme for color image is proposed in color quaternion wavelet transform (CQWT) domain. Compared with discrete wavelet transform (DWT), contourlet wavelet transform, and local binary patterns, CQWT processes a color image as a unit, and so, it can provide more forensics information to identify the photograph (PG) and computer generated (CG) images by considering the quaternion magnitude and phase measures. Meanwhile, two novel quaternion central moments for color images, i.e., quaternion skewness and kurtosis, are proposed to extract forensics features. In the condition of the same statistical model as Farid's model, the CQWT can boost the performance of the existing identification models. Compared with Farid's model and Li's model in 7500 PG and 7500 CG, the quaternion statistical features show a better classification performance. Results in the comparative experiments show that the classification accuracy of the CQWT improves by 19% more than Farid's model, and the quaternion features approximately improve by 2% more than the traditional.

In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts: domain-consistency used to measure the between-domain distribution discrepancy and class-consistency used to measure the within-domain class separability. The objective of transfer learning is to maximize the proposed metric, while for easily formulating this metric in model, we propose to minimize the domain-class-inconsistency, such that both domain distribution mismatch and class inseparability are well addressed. Two advantages of the proposed method are that on one hand the robust sparse coding selects a few valuable source data with noises (outliers) removed during knowledge transfer, and the proposed DCC metric can help to pursue discriminative subspaces of different domains for classification based transfer learning tasks. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art domain adaptation methods.