Backdoor attacks, which are a kind of emergent training-time threat to deep neural networks (DNNS). They can manipulate the output of DNNs and posses high insidiousness. In the field of natural language processing, some attack methods have been proposed and achieve very high attack success rates on multiple popular models. Nevertheless, the studies on defending textual backdoor defense are little conducted. In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and might be the first method that can handle all the attack situations. Experiments demonstrate the effectiveness of our model when blocking two latest backdoor attack methods.

ONION: A Simple and Effective Defense Against Textual Backdoor Attacks

This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attacker's capability and affected stage of the machine learning pipeline, the attack surfaces are recognized to be wide and then formalized into six categorizations: code poisoning, outsourcing, pretrained, data collection, collaborative learning and post-deployment. Accordingly, attacks under each categorization are combed. The countermeasures are categorized into four general classes: blind backdoor removal, offline backdoor inspection, online backdoor inspection, and post backdoor removal. Accordingly, we review countermeasures, and compare and analyze their advantages and disadvantages. We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor.Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks. In some cases, an attacker can intelligently bypass existing defenses with an adaptive attack. Drawing the insights from the systematic review, we also present key areas for future research on the backdoor, such as empirical security evaluations from physical trigger attacks, and in particular, more efficient and practical countermeasures are solicited.

Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review.

Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked. In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections. The results raise a serious alarm to the security of NLP models, which requires further research to be resolved. All the data and code of this paper are released at https://github.com/thunlp/BkdAtk-LWS.

/pdf/turn-the-combination-lock-learnable-textual-backdoor-attacks-3kc4lpy4cp.pdf

Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution

Backdoors can be injected to NLP models such that they misbehave when the trigger words or sentences appear in an input sample. Detecting such backdoors given only a subject model and a small number of benign samples is very challenging because of the unique nature of NLP applications, such as the discontinuity of pipeline and the large search space. Existing techniques work well for backdoors with simple triggers such as single character/word triggers but become less effective when triggers and models become complex (e.g., transformer models). We propose a new backdoor scanning technique. It transforms a subject model to an equivalent but differentiable form. It then uses optimization to invert a distribution of words denoting their likelihood in the trigger. It leverages a novel word discriminativity analysis to determine if the subject model is particularly discriminative for the presence of likely trigger words. Our evaluation on 3839 NLP models from the TrojAI competition and existing works with 7 state-of-art complex structures such as BERT and GPT, and 17 different attack types including two latest dynamic attacks, shows that our technique is highly effective, achieving over 0.9 detection accuracy in most scenarios and substantially outperforming two state-of-the-art scanners. Our submissions to TrojAI leaderboard achieve top performance in 2 out of the 3 rounds for NLP backdoor scanning.

Piccolo: Exposing Complex Backdoors in NLP Transformer Models

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model’s performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years. We start by categorizing the current threat models and attacks, and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning, and shed light on the current limitations and open research questions in this research field.

https://dl.acm.org/doi/pdf/10.1145/3585385

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

It has been proved that deep neural networks are facing a new threat called backdoor attacks, where the adversary can inject backdoors into the neural network model through poisoning the training dataset. When the input containing some special pattern called the backdoor trigger, the model with backdoor will carry out malicious task such as misclassification specified by adversaries. In text classification systems, backdoors inserted in the models can cause spam or malicious speech to escape detection. Previous work mainly focused on the defense of backdoor attacks in computer vision, little attention has been paid to defense method for RNN backdoor attacks regarding text classification. In this paper, through analyzing the changes in inner LSTM neurons, we proposed a defense method called Backdoor Keyword Identification (BKI) to mitigate backdoor attacks which the adversary performs against LSTM-based text classification by data poisoning. This method can identify and exclude poisoning samples crafted to insert backdoor into the model from training data without a verified and trusted dataset. We evaluate our method on four different text classification datset: IMDB, DBpedia ontology, 20 newsgroups and Reuters-21578 dataset. It all achieves good performance regardless of the trigger sentences.

Chuanshuai Chen

Papers

Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification

Mitigating backdoor attacks in LSTM-based text classification systems by Backdoor Keyword Identification