Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs

Although deep neural networks (DNNs) are known to be fragile, no one has studied the effects of zooming-in and zooming-out of images in the physical world on DNNs performance. In this paper, we demonstrate a novel physical adversarial attack technique called Adversarial Zoom Lens (AdvZL), which uses a zoom lens to zoom in and out of pictures of the physical world, fooling DNNs without changing the characteristics of the target object. The proposed method is so far the only adversarial attack technique that does not add physical adversarial perturbation attack DNNs. In a digital environment, we construct a data set based on AdvZL to verify the antagonism of equal-scale enlarged images to DNNs. In the physical environment, we manipulate the zoom lens to zoom in and out of the target object, and generate adversarial samples. The experimental results demonstrate the effectiveness of AdvZL in both digital and physical environments. We further analyze the antagonism of the proposed data set to the improved DNNs. On the other hand, we provide a guideline for defense against AdvZL by means of adversarial training. Finally, we look into the threat possibilities of the proposed approach to future autonomous driving and variant attack ideas similar to the proposed attack

Adversarial Neon Beam: Robust Physical-World Adversarial Attack to DNNs

In the physical world, light affects the performance of deep neural networks. Nowadays, many products based on deep neural network have been put into daily life. There are few researches on the effect of light on the performance of deep neural network models. However, the adversarial perturbations generated by light may have extremely dangerous effects on these systems. In this work, we propose an attack method called adversarial neon beam (AdvNB), which can execute the physical attack by obtaining the physical parameters of adversarial neon beams with very few queries. Experiments show that our algorithm can achieve advanced attack effect in both digital test and physical test. In the digital environment, 99.3% attack success rate was achieved, and in the physical environment, 100% attack success rate was achieved. Compared with the most advanced physical attack methods, our method can achieve better physical perturbation concealment. In addition, by analyzing the experimental data, we reveal some new phenomena brought about by the adversarial neon beam attack

Impact of Colour Variation on Robustness of Deep Neural Networks

Deep neural networks (DNNs) have have shown state-of-the-art performance for computer vision applications like image classification, segmentation and object detection. Whereas recent advances have shown their vulnerability to manual digital perturbations in the input data, namely adversarial attacks. The accuracy of the networks is significantly affected by the data distribution of their training dataset. Distortions or perturbations on color space of input images generates out-of-distribution data, which make networks more likely to misclassify them. In this work, we propose a color-variation dataset by distorting their RGB color on a subset of the ImageNet with 27 different combinations. The aim of our work is to study the impact of color variation on the performance of DNNs. We perform experiments on several state-of-the-art DNN architectures on the proposed dataset, and the result shows a significant correlation between color variation and loss of accuracy. Furthermore, based on the ResNet50 architecture, we demonstrate some experiments of the performance of recently proposed robust training techniques and strategies, such as Augmix, revisit, and free normalizer, on our proposed dataset. Experimental results indicate that these robust training techniques can improve the robustness of deep networks to color variation.Comment: arXiv admin note: substantial text overlap with arXiv:2209.0213

Fooling Thermal Infrared Detectors in Physical World

Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. However, few studies have explored the safety of infrared imaging systems in real-world settings. Previous research has used physical perturbations such as small bulbs and thermal "QR codes" to attack infrared imaging detectors, but such methods are highly visible and lack stealthiness. Other researchers have used hot and cold blocks to deceive infrared imaging detectors, but this method is limited in its ability to execute attacks from various angles. To address these shortcomings, we propose a novel physical attack called adversarial infrared blocks (AdvIB). By optimizing the physical parameters of the adversarial infrared blocks, this method can execute a stealthy black-box attack on thermal imaging system from various angles. We evaluate the proposed method based on its effectiveness, stealthiness, and robustness. Our physical tests show that the proposed method achieves a success rate of over 80% under most distance and angle conditions, validating its effectiveness. For stealthiness, our method involves attaching the adversarial infrared block to the inside of clothing, enhancing its stealthiness. Additionally, we test the proposed method on advanced detectors, and experimental results demonstrate an average attack success rate of 51.2%, proving its robustness. Overall, our proposed AdvIB method offers a promising avenue for conducting stealthy, effective and robust black-box attacks on thermal imaging system, with potential implications for real-world safety and security applications

Adversarial Color Projection: A Projector-Based Physical Attack to DNNs

Recent advances have shown that deep neural networks (DNNs) are susceptible to adversarial perturbations. Therefore, it is necessary to evaluate the robustness of advanced DNNs using adversarial attacks. However, traditional physical attacks that use stickers as perturbations are more vulnerable than recent light-based physical attacks. In this work, we propose a projector-based physical attack called adversarial color projection (AdvCP), which performs an adversarial attack by manipulating the physical parameters of the projected light. Experiments show the effectiveness of our method in both digital and physical environments. The experimental results demonstrate that the proposed method has excellent attack transferability, which endows AdvCP with effective blackbox attack. We prospect AdvCP threats to future vision-based systems and applications and propose some ideas for light-based physical attacks.Comment: arXiv admin note: substantial text overlap with arXiv:2209.0243

Corrigendum: Estimating the Minimal Number of Repeated Examinations for Random Responsiveness With the Coma Recovery Scale-Revised as an Example.

[This corrects the article DOI: 10.3389/fnint.2021.685627.]

Mathematical Simulation and program of gas-liquid two-phase well flow pattern

The judgment of flow pattern in the wellbore of a liquid-producing gas well is very important to the judgment of its liquid-carrying capacity in the production process. Through mathematical fitting of duns- ROS flow pattern of vertical well, Gould flow pattern of inclined well and Goiver flow pattern of horizontal well, the different flow pattern is fitted into 3, 4 and 4 curves respectively, and divided into 9, 10 and 20 regions respectively. The division standard among different regions is given, optimized parameters are selected, and a VB software is edited. The results show that the software fitting effect is accurate, and can be well applied to the actual flow pattern judgment

Major advancement in oil and gas exploration of Jurassic channel sandstone in Well Bazhong 1HF in northern Sichuan Basin and its significance

In January 2023, Well Bazhong 1HF in the northern Sichuan Basin obtained high-yield industrial oil flow of over 100 cubic meters from the Jurassic channel sandstone for the first time, realizing a major breakthrough. In order to provide more support for further oil and gas exploration in this area, this paper analyzes the sedimentary and reservoir characteristics of the Jurassic Lianggaoshan Formation in Bazhong region of northern Sichuan Basin and their control factors based on the exploration achievements of Well Bazhong 1HF. Then, oil and gas reservoir characteristics and oil and gas sources are comparatively analyzed. Finally, the key technologies for the exploration of channel sandstone oil and gas with multi-stage vertical superimposition, lateral migration, thin reservoir and strong heterogeneity are researched and developed, and the next oil and gas exploration direction in the Lianggaoshan Formation of northern Sichuan Basin is pointed out. And the following research results are obtained. First, in the second member of Lianggaoshan Formation (“Liang 2 Member” for short) in Bazhong region, multi-stage underwater distributary sand bodies of delta front are developed with sandstone thickness of about 25 m and average porosity of 5.6%. The pore types are mainly primary intergranular pores and feldspar/debris intragranular dissolved pores, the pore throat structure is good, and the development of good-quality reservoirs is controlled by the sedimentary microfacies of underwater distributary channel. Second, the oil and gas reservoir of Liang 2 Member is a highly oil-bearing condensate gas reservoir/volatile oil reservoir, whose oil and gas is mainly sourced from the semi-deep lacustrine shale of Liang 1 and 2 Members. Third, channel sand bodies are superimposed and developed continuously in the upper part of Liang 2 and Liang 3 Members, and the source-reservoir configuration is good, with the characteristics of near-source hydrocarbon accumulation and overpressure hydrocarbon enrichment. Fourth, for the channel sandstones with multi-stage vertical superimposition, lateral migration, thin reservoir and strong heterogeneity, the reservoir prediction technology of “high-frequency sequence stratigraphy slice, seismic frequency decomposition and facies-constrained seismic waveform indication inversion” is developed to precisely characterize the “sweet spot” target of narrow channel sandstone, and the key fracturing technology of “dense cutting + composite temporary plugging + high-intensity proppant injection + imbibition and oil-increasing” is formed to realize the large-scale reconstruction of channel sandstone reservoir. In conclusion, the breakthrough of Well Bazhong 1HF in the exploration of Lianggaoshan Formation oil and gas in Bazhong region reveals the huge potential of Jurassic oil and gas exploration in the Sichuan Basin, and plays a positive role in promoting the exploration and development of the Jurassic channel sandstone oil and gas in the Sichuan Basin

Study on critical liquid-carrying flow model in inclined Wells

It is very important to calculate the critical liquid-carrying flow for gas well.In this paper, an ellipsoid droplet model is established. By analyzing the droplet force in the inclined well and comprehensively considering the droplet drag force, gravity force, friction force, buoyancy force and supporting force, the general formula for calculating the critical liquid carrying flow is obtained. By using the reasonable formula of gas-liquid interfacial tension and The Formula of Weber number, the critical liquid carrying flow of gas well is verified

Preparation of particle-fixed silica monoliths used in capillary electrochromatography

Fused-silica capillaries were packed with porous 1 pin bare silica microspheres and immobilized by potassium silicate-formamide in order to obtain columns with silica-based monolithic packing. After curing, the particle-fixed monolithic columns were octadecylated in situ with dimethyloctadecylchlorosilane. The columns were mechanically strong and permeable. No noticeable loss in efficiency was found after using a column continuously for 1 month. The performances of the particle-fixed silica monolithic columns were evaluated for CEC under RP conditions. High separation efficiency (about 125200 plates/m) was obtained by using these new types of columns