The widespread adoption of face recognition has raised privacy concerns regarding the collection and use of facial data. To address this, researchers have explored "unlearnable examples" by adding imperceptible perturbations during model training to prevent the model from learning target features. However, current methods are inefficient and cannot guarantee transferability and robustness at the same time, causing impracticality in the real world. To remedy it, we introduce Side-information Guided Generative Unlearnable Examples (Segue). Using a once-trained multiple-used model to generate perturbations, Segue avoids the time-consuming gradient-based approach. To improve transferability, we introduce side information such as true or pseudo labels, which are inherently consistent across different scenarios. For robustness enhancement, a distortion layer is integrated into the training pipeline. Experiments show Segue is 1000× faster than previous methods, transferable across datasets and models, and resistant to JPEG compression, adversarial training, and standard augmentations.

Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs), enabling them to operate normally on clean inputs but manipulate predictions when specific trigger patterns occur. In this paper, we consider a practical post-training scenario backdoor defense, where the defender aims to evaluate whether a trained model has been compromised by backdoor attacks. Currently, post-training backdoor detection approaches often operate under the assumption that the defender has knowledge of the attack information, logit output from the model, and knowledge of the model parameters, limiting their implementation in practical scenarios.

The recent years have witnessed a great success of artificial intelligence applications in geological prospecting, so that the traditional manual work, which is time-consuming and labor-intensive, could be accomplished automatically or at least in a human–machine cooperation way. This letter presents a first attempt in proposing an automatic way to predict the cross-well sandstone that plays a crucial role in formation characterization and reservoir exploration. Such a two-stage framework is composed of i) a convolution neural network (CNN)-based coarse prediction module and ii) a geological experience-based error correction (EC) module. Experiments demonstrate that our proposed module can achieve comparable accuracy as experts.

Subsurface lithofacies classification refers to the way of establishing a classifier on the interpreted well logging data to predict the lithofacies types corresponding to the uninterpreted ones. Such a task has become a research focus by applying machine learning technologies under the assumption of independent and identical distribution. However, due to the differences in, such as sedimentary environments, reservoir heterogeneity, and logging equipments, the same lithofacies might exhibit different logging characteristics between two different wells or even two different strata in one well. Therefore, motivated by the data drift issue and inspired by the domain adaptation methods, we propose the robust unilateral alignment (RUA) for lithofacies classification. The characteristics of the proposed RUA are as follows: 1) the projected maximum mean discrepancy (PMMD) is designed to reduce the marginal and conditional distribution discrepancy; 2) the random data mapping and target domain information preserving constraint is adopted to embody the data transformation model; and 3) the weighting mechanism and risk-aware constraint are introduced to solve the class imbalance and iterative risk problems. The experiments conducted on the data sets from Jiyang Depression, Bohai Bay Basin, verify the superior performances in accuracy and stability over the existing work.

Identifying borehole lithofacies through geop- hysical loggings is a fundamental task in petroleum exploration industry. Recent interdisciplinary studies have demonstrated the feasibility of applying machine learning to lithofacies identification. Most of these studies establish a mapping from the logging values at one depth point to the lithofacies type. However, due to the intrinsic properties of geophysical loggings, the logging shape should be taken into consideration, apart from the absolute values. In this article, we present the attempt to predict the lithofacies by feeding logging segments, and for the first time model the logging lithofacies identification problem as 1-D semantic segmentation. Such a logging segmentation task is challenging due to two reasons, strong spatial heterogeneity of lithofacies subsurface distribution and the explicit physical significance of geophysical loggings. To solve these challenges, we propose a novel geophysical logging segmentation network entitled SegLog. Specifically, we develop a global statistics pooling subnetwork and a statistics fusion subnetwork to generate statistical embeddings of geophysical loggings. Based on these statistical embeddings, we design a pixel-enhanced convolutional subnetwork to learn the microdetailed features, indicated by pixel-level logging values. These features are fused with the macrosemantic features extracted by a backbone U-Net to constitute the representations that can simultaneously describe the logging spatial correlation and pixel specificity. Experimental results on two logging datasets from the Jiyang Depression verify the effectiveness of our modeling strategy and its state-of-the-art performance on the lithofacies identification problem.