Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.Comment: accepted by TPAMI202

Enhancing Transferability of Adversarial Examples with Spatial Momentum

Many adversarial attack methods achieve satisfactory attack success rates under the white-box setting, but they usually show poor transferability when attacking other DNN models. Momentum-based attack is one effective method to improve transferability. It integrates the momentum term into the iterative process, which can stabilize the update directions by adding the gradients' temporal correlation for each pixel. We argue that only this temporal momentum is not enough, the gradients from the spatial domain within an image, i.e. gradients from the context pixels centered on the target pixel are also important to the stabilization. For that, we propose a novel method named Spatial Momentum Iterative FGSM attack (SMI-FGSM), which introduces the mechanism of momentum accumulation from temporal domain to spatial domain by considering the context information from different regions within the image. SMI-FGSM is then integrated with temporal momentum to simultaneously stabilize the gradients' update direction from both the temporal and spatial domains. Extensive experiments show that our method indeed further enhances adversarial transferability. It achieves the best transferability success rate for multiple mainstream undefended and defended models, which outperforms the state-of-the-art attack methods by a large margin of 10\% on average.Comment: Accepted as Oral by 5-th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 202

Application of a novel phage LPSEYT for biological control of Salmonella in foods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Salmonella is a leading cause of foodborne diseases, and in recent years, many isolates have exhibited a high level of antibiotic resistance, which has led to huge pressures on public health. Phages are a promising strategy to control food‐borne pathogens. In this study, one of our environmental phage isolates, LPSEYT, was to be able to restrict the growth of zoonotic Salmonella enterica in vitro over a range of multiplicity of infections. Phage LPSEYT exhibited wide‐ranging pH and thermal stability and rapid reproductive activity with a short latent period and a large burst size. Phage LPSEYT demonstrated potential efficiency as a biological control agent against Salmonella in a variety of food matrices, including milk and lettuce. Morphological observation, comparative genomic, and phylogenetic analysis revealed that LPSEYT does not belong to any of the currently identified genera within the Myoviridae family, and we suggest that LPSEYT represents a new genus, the LPSEYTvirus. This study contributes a phage database, develops beneficial phage resources, and sheds light on the potential application value of phages LPSEYT on food safety

Microbial Communities in the Lungs of Bats in China

Bats are the hosts of multiple pathogens, but the microbial composition of their lung tissues remains unknown. Our study investigated the species compositions and genera of important respiratory tract pathogenic bacteria in bat lung tissue. A microbiota study was conducted in Hebei, Henan and Guizhou provinces in China. Lung tissues were collected from 104 healthy bats. The lung tissue was subjected to 16S ribosomal ribonucleic acid gene sequencing. We obtained 7,708,734 high-quality bacterial sequences from 104 healthy bats. Overall, the annotations indicated 55 phyla, 73 classes, 164 orders, 322 families and 953 genera. The lung microbiota was highly polymorphic and variable among bats from Hebei, Henan and Guizhou. The genetic characteristics of the main recognized respiratory pathogens in the samples were analyzed. The findings indicate that the lungs of bats carry numerous bacteria with pathogenic importance. Pathogens disseminate through the respiratory tract in bats and are widely distributed among bats. Because bats prefer to inhabit areas placing them in close contact with humans, such as eaves and old buildings, further investigations are warranted to identify bat microbiota and their potential effects on humans