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Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community. Backdoor defenses are mainly based on backdoor inversion, which has been shown to be generic, model-agnostic, and applicable to practical threat scenarios. State-of-the-art backdoor inversion recovers a mask in the feature space to locate prominent backdoor features, where benign and backdoor features can be disentangled. However, it suffers from high computational overhead, and we also find that it overly relies on prominent backdoor features that are highly distinguishable from benign features. To tackle these shortcomings, this paper improves backdoor feature inversion for backdoor detection by incorporating extra neuron activation information. In particular, we adversarially increase the loss of backdoored models with respect to weights to activate the backdoor effect, based on which we can easily differentiate backdoored and clean models. Experimental results demonstrate our defense, BAN, is 1.37$\times$ (on CIFAR-10) and 5.11$\times$ (on ImageNet200) more efficient with 9.99% higher detect success rate than the state-of-the-art defense BTI-DBF. Our code and trained models are publicly available.\url{https://anonymous.4open.science/r/ban-4B32}

We perform a comprehensive CO study toward the Monoceros OB1 (Mon OB1) region based on the MWISP survey at an angular resolution of about $50''$. The high-sensitivity data, together with the high dynamic range, shows that molecular gas in the $\rm 8^{\circ}\times4^{\circ}$ region displays complicated hierarchical structures and various morphology (e.g., filamentary, cavity-like, shell-like, and other irregular structures). Based on Gaussian decomposition and clustering for $\mathrm{^{13}CO}$ data, a total of 263 $\mathrm{^{13}CO}$ structures are identified in the whole region, and 88% of raw data flux is recovered. The dense gas with relatively high column density from the integrated CO emission is mainly concentrated in the region where multiple $\rm ^{13}CO$ structures are overlapped. Combining the results of 32 large $\mathrm{^{13}CO}$ structures with distances from Gaia DR3, we estimate an average distance of $\rm 729^{+45}_{-45}~pc$ for the GMC complex. The total mass of the GMC Complex traced by $\mathrm{^{12}CO}$, $\mathrm{^{13}CO}$, and $\mathrm{C^{18}O}$ are $1.1\times10^5~M_\odot$, $4.3\times10^4~M_\odot$, and $8.4\times10^3~M_\odot$, respectively. The dense gas fraction shows a clear difference between Mon OB1 GMC East (12.4%) and Mon OB1 GMC West (3.3%). Our results show that the dense gas environment is closely linked to the nearby star-forming regions. On the other hand, star-forming activities have a great influence on the physical properties of the surrounding molecular gas (e.g., greater velocity dispersion, higher temperatures, and more complex velocity structures, etc.). We also discuss the distribution/kinematics of molecular gas associated with nearby star-forming activities., Comment: 28 pages, 17 figures, match to the version of APJ, 966, 202. The dataset has been released on https://doi.org/10.57760/sciencedb.17451

The lightcurves of asteroids are essential for determining their physical characteristics, including shape, spin, size, and surface composition. However, most asteroids are missing some of these basic physical parameters due to lack of photometric data. Although a few telescopes or surveys are specially designed for photometric lightcurve observations of asteroids, many ground-based and space-based sky surveys for hunting new exoplanets, transient events, etc., should capture numerous small Solar System objects. This will benefit the physical studies of these objects. In order to extract data of these moving objects from time-domain photometric surveys, we have developed a new method using the model tree algorithm in the field of machine learning. A dedicated module is built to automatically identify moving objects in dataset, and extract their photometric and astrometric data. As the first application of this novel method, we have analyzed data in five fields of the Yunnan-Hong Kong wide field photometric (YNHK) survey, from which 538 lightcurves of 211 asteroids are successfully extracted. Meanwhile, we also tested the method based on the data from NASA's Transiting Exoplanet Survey Satellite, and the result proves the reliability of our method. With derived lightcurves of 13 asteroids from the YNHK survey, we have determined their synodic spin periods, among which the periods of 4 asteroids are estimated for the first time. In future, we are going to apply this method to search for small objects in the outer part of the Solar System from the Chinese Space Station Telescope survey.

Vision Transformers (ViTs) achieve excellent performance in various tasks, but they are also vulnerable to adversarial attacks. Building robust ViTs is highly dependent on dedicated Adversarial Training (AT) strategies. However, current ViTs' adversarial training only employs well-established training approaches from convolutional neural network (CNN) training, where pre-training provides the basis for AT fine-tuning with the additional help of tailored data augmentations. In this paper, we take a closer look at the adversarial robustness of ViTs by providing a novel theoretical Mutual Information (MI) analysis in its autoencoder-based self-supervised pre-training. Specifically, we show that MI between the adversarial example and its latent representation in ViT-based autoencoders should be constrained by utilizing the MI bounds. Based on this finding, we propose a masked autoencoder-based pre-training method, MIMIR, that employs an MI penalty to facilitate the adversarial training of ViTs. Extensive experiments show that MIMIR outperforms state-of-the-art adversarially trained ViTs on benchmark datasets with higher natural and robust accuracy, indicating that ViTs can substantially benefit from exploiting MI. In addition, we consider two adaptive attacks by assuming that the adversary is aware of the MIMIR design, which further verifies the provided robustness.

In this paper, we propose a novel method, IB-RAR, which uses Information Bottleneck (IB) to strengthen adversarial robustness for both adversarial training and non-adversarial-trained methods. We first use the IB theory to build regularizers as learning objectives in the loss function. Then, we filter out unnecessary features of intermediate representation according to their mutual information (MI) with labels, as the network trained with IB provides easily distinguishable MI for its features. Experimental results show that our method can be naturally combined with adversarial training and provides consistently better accuracy on new adversarial examples. Our method improves the accuracy by an average of 3.07% against five adversarial attacks for the VGG16 network, trained with three adversarial training benchmarks and the CIFAR-10 dataset. In addition, our method also provides good robustness for undefended methods, such as training with cross-entropy loss only. Finally, in the absence of adversarial training, the VGG16 network trained using our method and the CIFAR-10 dataset reaches an accuracy of 35.86% against PGD examples, while using all layers reaches 25.61% accuracy.

Deep learning models achieve excellent performance in numerous machine learning tasks. Yet, they suffer from security-related issues such as adversarial examples and poisoning (backdoor) attacks. A deep learning model may be poisoned by training with backdoored data or by modifying inner network parameters. Then, a backdoored model performs as expected when receiving a clean input, but it misclassifies when receiving a backdoored input stamped with a pre-designed pattern called "trigger". Unfortunately, it is difficult to distinguish between clean and backdoored models without prior knowledge of the trigger. This paper proposes a backdoor detection method by utilizing a special type of adversarial attack, universal adversarial perturbation (UAP), and its similarities with a backdoor trigger. We observe an intuitive phenomenon: UAPs generated from backdoored models need fewer perturbations to mislead the model than UAPs from clean models. UAPs of backdoored models tend to exploit the shortcut from all classes to the target class, built by the backdoor trigger. We propose a novel method called Universal Soldier for Backdoor detection (USB) and reverse engineering potential backdoor triggers via UAPs. Experiments on 345 models trained on several datasets show that USB effectively detects the injected backdoor and provides comparable or better results than state-of-the-art methods.

We present wide-field and high-sensitivity CO(1-0) molecular line observations toward the Cassiopeia region, using the 13.7m millimeter telescope of the Purple Mountain Observatory (PMO). The CO observations reveal a large-scale highly filamentary molecular cloud within the Galactic region of 132\fdg0\,$\geq$\,$l$\,$\geq$\,122\fdg0 and -1\fdg0\,$\leq$\,$b$\,$\leq$\,3\fdg0 and the velocity range from approximately +1 to +4 km/s. The measured length of the large-scale filament, referred to as the Cassiopeia Filament, is about 390 pc. The observed properties of the Cassiopeia Filament, such as length, column density, and velocity gradient, are consistent with those synthetic large-scale filaments in the inter-arm regions. Based on its observed properties and location on the Galactic plane, we suggest that the Cassiopeia Filament is a spur of the Local arm, which is formed due to the galactic shear. The western end of the Cassiopeia Filament shows a giant arc-like molecular gas shell, which is extending in the velocity range from roughly -1 to +7 km/s. Finger-like structures, with systematic velocity gradients, are detected in the shell. The CO kinematics suggest that the large shell is expanding at a velocity of ~6.5 km/s. Both the shell and finger-like structures outline a giant bubble with a radius of ~16 pc, which is likely produced by stellar wind from the progenitor star of a supernova remnant. The observed spectral linewidths suggest that the whole Cassiopeia Filament was quiescent initially until its west part was blown by stellar wind and became supersonically turbulent., Comment: 46 pages, 19 figures, to be published by the AJ