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Neural Cleanse: Identifying and Mitigating Backdoor Attacks in

Deep neural networks (DNNs) today play an integral role in a wide range of critical applications from classification systems like facial and iris recognition

A Natural Backdoor Attack on Deep Neural Networks

Backdoor attacks have also been found possible in federated learning [1. 48

Hidden Trigger Backdoor Attacks

2018. Backdoor embedding in convolutional neural network models via invisible perturbation. arXiv:1808.10307. Liu Y.; Ma

Latent Backdoor Attacks on Deep Neural Networks

ral networks; Artificial intelligence; Machine learning;. KEYWORDS neural networks; backdoor attacks. ACM Reference Format: Yuanshun Yao Huiying Li

Invisible Backdoor Attacks on Deep Neural Networks via

Abstract—Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks where hidden features (patterns) trained.

Detecting Backdoor Attacks on Deep Neural Networks by Activation

One recent and particularly insidious type of poisoning at- tack generates a backdoor or trojan in a deep neural network. (DNN) (Gu et al. 2017; Liu et al.

CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks

The data poisoning attack has raised serious security concerns on the safety of deep neural networks since it can lead to neural backdoor that 

Handcrafted Backdoors in Deep Neural Networks

8 juin 2021 handcrafted backdoors—to the neural network supply-chain. Our handcrafted backdoor attacks directly modify a pre-.

Composite Backdoor Attack for Deep Neural Network by Mixing

With the prevalent use of Deep Neural Networks (DNNs) in many applications security of these networks is of importance. Pre- trained DNNs may contain backdoors 

Robust Backdoor Attacks against Deep Neural Networks in Real

door attacks Deep neural networks

Handcrafted Backdoors in Deep Neural Networks - arXivorg

Backdooring attacks [17] target the supply-chain of neural network training to inject malicious hid-den behaviors into a model Most prior work studies the same objective: modify the neural network fso that when it is presented with a “triggered input” x0 the classi?cation f(x0) is incorrect Con-

Robust Backdoor Attacks against Deep Neural Networks in Real

Abstract—Deep neural networks (DNN) have been widelydeployed in various applications However many researchesindicated that DNN is vulnerable to backdoor attacks Theattacker can create a hidden backdoor in target DNN model andtrigger the malicious behaviors by submitting speci?c backdoorinstance

Detecting Backdoor Attacks on Deep Neural Networks by

11:end for Algorithm 1: Backdoor Detection Activation Clustering Al- gorithm Our method described more formally by Algorithm 1 uses this insight to detect poisonous data in the following way First the neural network is trained using untrusted data that potentially includes poisonous samples

Backdoor Embedding in Convolutional Neural Network Models via

perturbation mask as backdoor i e patterned static perturbation mask and targeted adaptive perturbation mask which can be eas-ily added to image samples and injected into the learning model subsequently Second apart from being hardly noticeable visually the injection of the backdoor only minutely impairs normal behav-

Is there a backdoor in a deep neural network?

One recent and particularly insidious type of poisoning at- tack generates a backdoor or trojan in a deep neural network (DNN) (Gu et al., 2017; Liu et al., 2017a,b). DNNs compro- mised in this manner perform very well on standard validation and test samples, but behave badly on inputs having a spe- ci?c backdoor trigger.

Can deep neural network solve direction-of-arrival (DOA) problem?

Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application.

Can neural networks be trained with backpropagation?

However, as their learning algorithms lag behind conventional neural networks trained with backpropagation, not many applications can be found today. The highest levels of accuracy can be achieved by converting networks that are trained with backpropagation to spiking networks.

How are neural networks used in real-world business applications?

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualize complex databases for marketing segmentation.