Attention guided global enhancement and local refinement network for semantic segmentation

The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated-FCN model for two major problems. First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts. Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. To tackle these challenges, a Global Enhancement Method is proposed to aggregate global information from high-level feature maps and adaptively distribute them to different decoder layers, alleviating the shortage of global contexts in the upsampling process. Besides, a Local Refinement Module is developed by utilizing the decoder features as the semantic guidance to refine the noisy encoder features before the fusion of these two (the decoder features and the encoder features). Then, the two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed. Extensive experiments on PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the effectiveness of the proposed approach. In particular, with a vanilla ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean IoU) on the PASCAL Context dataset. The code is available at https://github.com/zhasen1996/AGLN.Comment: 12 pages, 6 figure

Unveiling Single-Bit-Flip Attacks on DNN Executables

Recent research has shown that bit-flip attacks (BFAs) can manipulate deep neural networks (DNNs) via DRAM Rowhammer exploitations. Existing attacks are primarily launched over high-level DNN frameworks like PyTorch and flip bits in model weight files. Nevertheless, DNNs are frequently compiled into low-level executables by deep learning (DL) compilers to fully leverage low-level hardware primitives. The compiled code is usually high-speed and manifests dramatically distinct execution paradigms from high-level DNN frameworks. In this paper, we launch the first systematic study on the attack surface of BFA specifically for DNN executables compiled by DL compilers. We design an automated search tool to identify vulnerable bits in DNN executables and identify practical attack vectors that exploit the model structure in DNN executables with BFAs (whereas prior works make likely strong assumptions to attack model weights). DNN executables appear more "opaque" than models in high-level DNN frameworks. Nevertheless, we find that DNN executables contain extensive, severe (e.g., single-bit flip), and transferrable attack surfaces that are not present in high-level DNN models and can be exploited to deplete full model intelligence and control output labels. Our finding calls for incorporating security mechanisms in future DNN compilation toolchains.Comment: Fix typ

Phase interaction induced texture in a plasma sprayed-remelted NiCrBSi coating during solidification: An electron backscatter diffraction study

Although considerable endeavors have been dedicated to investigate the microstructures of the remelting-enhanced NiCrBSi coatings, the textures in the remelted coatings, which may result in property anisotropy, are rarely studied. In this work, the recrystallized fractions, grain orientations and interphase boundaries for Ni, Ni3B and CrB in a plasma sprayed-remelted NiCrBSi coating were investigated by electron backscatter diffraction. The results demonstrate that the texture is induced by phase interaction during solidification. Cooling from the liquid, the firstly formed Ni grains possess a cube fiber texture of {001}〈001〉. The successively formed Ni3B colonies are randomly oriented and keep specific orientation relationships with the surrounding Ni grains, resulting in formation of some weak texture components of Ni. The finally formed CrB grains have a considerably high frequency (40.8%) of lattice correlation boundary of (002)Ni//(040)CrB, but no specific orientation relationships with Ni3B grains. Hence, the interaction of Ni and CrB grains leads to the formation of more texture components of Ni. As such, the phase interaction induced texture forms in the remelted NiCrBSi coating. This work would give an insight into the anisotropy in the remelted NiCrBSi coatings and provide a theoretical basis of further optimizing the remelting process technologies