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"FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning" by Zhiyu Zhu, Huaming Chen et al.

The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of net ....

Adversarial Attack , Ssessing Neuron Importance , Ttribution Algorithm , Deep Neural Network , Fine Tuning ,

"DANAA: Towards Transferable Attacks with Double Adversarial Neuron Att" by Zhibo Jin, Zhiyu Zhu et al.

While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attacks are one of the effective attack types, which target the learned features in the hidden layers to improve their transferability across different models. Yet it is observed that the transferability has been largely impacted by the neuron importance estimation results. In this paper, a double adversarial neuron attribution attack method, termed ‘DANAA’, is proposed to obtain more accurate feature importance estimation. In our method, the model outputs are attributed to the middle layer based on an adversarial non-linear path. The goal is to measure the weight of individual neurons and retain the features that are more important toward transferability. We have conducted extensive experiments on the benchmark datasets to demonstrate the state-of-the-art performance of our method. Our code is available at: ht ....

Adversarial Attack , Ttribution Based Attack ,

"Boost Off/On-Manifold Adversarial Robustness for Deep Learning with La" by Mengdie Huang, Yi Xie et al.

Deep neural networks excel at solving intuitive tasks that are hard to describe formally, such as classification, but are easily deceived by maliciously crafted samples, leading to misclassification. Recently, it has been observed that the attack-specific robustness of models obtained through adversarial training does not generalize well to novel or unseen attacks. While data augmentation through mixup in the input space has been shown to improve the generalization and robustness of models, there has been limited research progress on mixup in the latent space. Furthermore, almost no research on mixup has considered the robustness of models against emerging on-manifold adversarial attacks. In this paper, we first design a latent-space data augmentation strategy called dual-mode manifold interpolation, which allows for interpolating disentangled representations of source samples in two modes: convex mixing and binary mask mixing, to synthesize semantic samples. We then propose a resilien ....

Latentrepresentationmixup Larepmixup , Adversarial Attack , Adversarial Robustness , Deep Neural Networks , Representation Learning ,

"Data Poisoning Attack Using Hybrid Particle Swarm Optimization in Conn" by Chi Cui, Haiping Du et al.

The development of connected and autonomous vehicles (CAV s) relies heavily on deep learning technology, which has been widely applied to perform a variety of tasks in CAYs. On the other hand, deep learning faces some security concerns. Data poisoning attack, as one of the security threats, can compromise the deep learning models by injecting poisoned training samples. The poisoned models may make more false predictions, and may cause fatal accidents of CA V s in the worst case. Therefore, the principles of poisoning attacks are worth studying in order to propose counter measures. In this work, we propose a black-box and clean-label data poisoning attack method that uses hybrid particle swarm optimization with simulated annealing to generate perturbations for poisoning. The attacking method is evaluated by experiments on the deep learning models of traffic sign recognition systems on CA V s, and the results show that the classification accuracies of the target deep learning models are ....

Adversarial Attack , Onnected And Autonomous Vehicles , Data Poisoning ,

Paper: How to fool share-trading bots with retweets

Paper: How to fool share-trading bots with retweets
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