Inspired by consistency of visual saliency between different vision models, a surrogate model isexpected toimprovetheattack performance viatransferability.

Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially. However, designing the structures of different attention operations requires a bulk of computation and extensive expertise.

The results on CIFAR10 were listed in Table R1. It reveals that HOGA searched by AutoLA (k=4)) still outperforms SE and CBAM by a large margin. We further customized SE and CBAM using the group split operation (denoted by "HOG"), resulting in a specific The HOGA searched by AutoLA outperforms its randomly search counterparts (denoted by "Rand"). We tested the generalization ability of HOGA searched on ResNet56 (denoted by "AutoLA_56") WiderResNet, indicating the consistent superiority of the HOGA searched by AutoLA over previous attention methods. We also compared AutoLA with SE and CBAM on a larger backbone (e.g., The results in Table R3 suggest that AutoLA still outperforms other attention modules.

In Figure A1, we illustrate the gradients from Inception-V3 [15] and ResNet-152 [9]. These authors have contributed equally. Output: updated surrogate model S . The experiment setting and images are same as previous state-of-the-art [2]. Thereby, we also report the A VG.Q' including failures (in Table A1, A2, A3, A4, A5), where failure query numbers are considered as 10,000.

While graph neural networks (GNNs) have gained popularity for learning circuit representations in various electronic design automation (EDA) tasks, they face challenges in scalability when applied to large graphs and exhibit limited generalizability to new designs. These limitations make them less practical for addressing large-scale, complex circuit problems. In this work we propose HOGA, a novel attention-based model for learning circuit representations in a scalable and generalizable manner. HOGA first computes hop-wise features per node prior to model training. Subsequently, the hop-wise features are solely used to produce node representations through a gated self-attention module, which adaptively learns important features among different hops without involving the graph topology. As a result, HOGA is adaptive to various structures across different circuits and can be efficiently trained in a distributed manner. To demonstrate the efficacy of HOGA, we consider two representative EDA tasks: quality of results (QoR) prediction and functional reasoning. Our experimental results indicate that (1) HOGA reduces estimation error over conventional GNNs by 46.76% for predicting QoR after logic synthesis; (2) HOGA improves 10.0% reasoning accuracy over GNNs for identifying functional blocks on unseen gate-level netlists after complex technology mapping; (3) The training time for HOGA almost linearly decreases with an increase in computing resources.

This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available. Inspired by consistency of visual saliency between different vision models, a surrogate model is expected to improve the attack performance via transferability. By combining transferability-based and query-based black-box attack, we propose a surprisingly simple baseline approach (named SimBA++) using the surrogate model, which significantly outperforms several state-of-the-art methods. Moreover, to efficiently utilize the query feedback, we update the surrogate model in a novel learning scheme, named High-Order Gradient Approximation (HOGA). By constructing a high-order gradient computation graph, we update the surrogate model to approximate the victim model in both forward and backward pass. The SimBA++ and HOGA result in Learnable Black-Box Attack (LeBA), which surpasses previous state of the art by considerable margins: the proposed LeBA significantly reduces queries, while keeping higher attack success rates close to 100% in extensive ImageNet experiments, including attacking vision benchmarks and defensive models. Code is open source at https://github.com/TrustworthyDL/LeBA.