Detecting boundary points (including outliers) is often more interesting than detecting normal observations, since they represent valid, interesting, and potentially valuable patterns. Since data representation can uncover the intrinsic data structure, we present an efficient representation-based method for detecting such points, which are generally located around the margin of densely distributed data, such as a cluster. For each point, the negative components in its representation generally correspond to the boundary points among its affine combination of points. In the presented method, the reverse unreachability of a point is proposed to evaluate to what degree this observation is a boundary point. The reverse unreachability can be calculated by counting the number of zero and negative components in the representation. The reverse unreachability explicitly takes into account the global data structure and reveals the disconnectivity between a data point and other points. This paper reveals that the reverse unreachability of points with lower density has a higher score. Note that the score of reverse unreachability of an outlier is greater than that of a boundary point. The top-m ranked points can thus be identified as outliers. The greater the value of the reverse unreachability, the more likely the point is a boundary point. Compared with related methods, our method better reflects the characteristics of the data, and simultaneously detects outliers and boundary points regardless of their distribution and the dimensionality of the space. Experimental results obtained for a number of synthetic and realworld data sets demonstrate the effectiveness and efficiency of our method.

Outlier detection has drawn significant interest from both academia and industry, such as network intrusion detection. Most existing methods implicitly or explicitly rely on distances in Euclidean space. However, the Euclidean distance may be incapable of measuring the similarity among high-dimensional data due to the curse of dimensionality, thus leading to inferior performance in practice. This paper presents an innovative approach for outlier detection from the view of meaningful structure scores. If two points have similar features, the difference between their structural scores is small and vice versa. The scores are calculated by measuring the variance of angles weighted by data representation, which takes the global data structure into the measurement. Thus, it could consistently rank more similar points. Compared with existing methods, our structural scores could be better to reflect the characteristics of data in a high-dimensional space. The proposed method consistently ranks more similar points. Experiments on synthetic and several real-world datasets have demonstrated the effectiveness and efficiency of our proposed methods.

The rapid advancement of generative models has profoundly transformed the field of digital content creation, bringing unprecedented opportunities for media generation. However, the widespread adoption of this technology has also led to the emergence of highly realistic fake facial images and videos, which pose significant threats to public trust and societal security. To address the challenges of deepfake detection, this paper proposes a novel method based on Spatial-Frequency Feature Integration (SFFI), which effectively identifies fake content by combining spatial and frequency features of images. Additionally, to tackle the issue of class imbalance in the datasets, we propose an Authenticity-Aware Margin Loss (AAML). This loss function dynamically adjusts the decision boundary to enhance the model’s ability to recognize minority class samples. The proposed method was trained and evaluated on four challenging datasets: FaceForensics++, Celeb-DF v1, Celeb-DF v2, and the DeepFake Detection Challenge Preview, and compared against ten state-of-the-art methods. Experimental results demonstrate that the proposed method consistently outperforms all existing approaches across all datasets.