Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through graph augmentation and multi-scale contrast modules. However, the underlying mechanisms of how these modules work have not been fully explored. We dive into the multi-scale and graph augmentation mechanism and observed that multi-scale contrast modules do not enhance the expression, while the multi-GNN modules are the hidden contributors. Previous studies have tended to attribute the benefits brought by multi-GNN to the multi-scale modules. In the paper, we delve into the misconception and propose Multi-GNN and Augmented Graph contrastive framework MAG, which unified the existing GCAD methods in the contrastive self-supervised perspective. We extracted two variants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight instance of the MAG, which outperform the state-of-the-art on Cora and Pubmed with the low computational cost. The variant M-MAG equipped with multi-GNN modules further improve the detection performance. Our study sheds light on the drawback of the existing GCAD methods and demonstrates the potential of multi-GNN and graph augmentation modules. Our code is available at https://anonymous.4open.science/r/MAG-Framework-74D0.

Deep steganography (DS), using neural networks to hide one image in another, has performed well in terms of invisibility, embedding capacity, etc. Current steganalysis methods for DS can only detect or remove secret images hidden in natural images and cannot analyze or modify secret content. Our technique is the first approach to not only effectively prevent covert communications using DS, but also analyze and modify the content of covert communications. We proposed a novel adversarial attack method for DS considering both white-box and black-box scenarios. For the white-box attack, several novel loss functions were applied to construct a gradient- and optimizer-based adversarial attack that could delete and modify secret images. As a more realistic case, a black-box method was proposed based on surrogate training and a knowledge distillation technique. All methods were tested on the Tiny ImageNet and MS COCO datasets. The experimental results showed that the proposed attack method could completely remove or even modify the secret image in the container image while maintaining the latter’s high quality. More importantly, the proposed adversarial attack method can also be regarded as a new DS approach.

The broad learning system (BLS) is a brief, flat neural network structure that has shown effectiveness in various classification tasks. However, original input data with high dimensionality often contain superfluous and correlated information affecting recognition performance. Moreover, the large number of randomly mapped feature nodes and enhancement nodes may also cause a risk of redundant information that interferes with the conciseness and performance of the broad learning paradigm. To address the above-mentioned issues, we aim to introduce a broad learning model with a dual feature extraction strategy (BLM_DFE). In particular, kernel principal component analysis (KPCA) is applied to process the original input data before extracting effective low-dimensional features for the broad learning model. Afterwards, we perform KPCA again to simplify the feature nodes and enhancement nodes in the broad learning architecture to obtain more compact nodes for classification. As a result, the proposed model has a more straightforward structure with fewer nodes and retains superior recognition performance. Extensive experiments on diverse datasets and comparisons with various popular classification approaches are investigated and evaluated to support the effectiveness of the proposed model (e.g., achieving the best result of 77.28%, compared with 61.44% achieved with the standard BLS, on the GT database).

The field of object detection has witnessed significant advancements in recent years, thanks to the remarkable progress in artificial intelligence and deep learning. These breakthroughs have significantly enhanced the accuracy and efficiency of detecting and categorizing objects in digital images. Nonetheless, contemporary object detection technologies have certain limitations, such as their inability to counter white-box attacks, insufficient denoising, suboptimal reconstruction, and gradient confusion. To overcome these hurdles, this study proposes an innovative approach that uses conditional diffusion models to perturb adversarial examples. The process begins with the application of a random chessboard mask to the adversarial example, followed by the addition of a slight noise to fill the masked area during the forward process. The adversarial image is then restored to its original form through a reverse generative process that only considers the masked pixels, not the entire image. Next, we use the complement of the initial mask as the mask for the second stage to reconstruct the image once more. This two-stage masking process allows for the complete removal of global disturbances and aids in image reconstruction. In particular, we employ a conditional diffusion model based on a class-conditional U-Net architecture, with the source image further conditioned through concatenation. Our method outperforms the recently introduced HARP method by 5% and 6.5% in mAP on the COCO2017 and PASCAL VOC datasets, respectively, under non-APT PGD attacks. Comprehensive experimental results confirm that our method can effectively restore adversarial examples, demonstrating its practical utility.

Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.