NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~x5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems.

NIP: Neuron-level Inverse Perturbation Against Adversarial Attacks

Although deep learning models have achieved unprecedented success, their vulnerabilities towards adversarial attacks have attracted increasing attention, especially when deployed in security-critical domains. To address the challenge, numerous defense strategies, including reactive and proactive ones, have been proposed for robustness improvement. From the perspective of image feature space, some of them cannot reach satisfying results due to the shift of features. Besides, features learned by models are not directly related to classification results. Different from them, We consider defense method essentially from model inside and investigated the neuron behaviors before and after attacks. We observed that attacks mislead the model by dramatically changing the neurons that contribute most and least to the correct label. Motivated by it, we introduce the concept of neuron influence and further divide neurons into front, middle and tail part. Based on it, we propose neuron-level inverse perturbation(NIP), the first neuron-level reactive defense method against adversarial attacks. By strengthening front neurons and weakening those in the tail part, NIP can eliminate nearly all adversarial perturbations while still maintaining high benign accuracy. Besides, it can cope with different sizes of perturbations via adaptivity, especially larger ones. Comprehensive experiments conducted on three datasets and six models show that NIP outperforms the state-of-the-art baselines against eleven adversarial attacks. We further provide interpretable proofs via neuron activation and visualization for better understanding.

Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction

Dynamic link prediction (DLP) makes graph prediction based on historical information. Since most DLP methods are highly dependent on the training data to achieve satisfying prediction performance, the quality of the training data is crucial. Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i.e., generating a subgraph sequence as the trigger and embedding it to the training data. However, the vulnerability of DLP toward backdoor attacks has not been studied yet. To address the issue, we propose a novel backdoor attack framework on DLP, denoted as Dyn-Backdoor. Specifically, Dyn-Backdoor generates diverse initial-triggers by a generative adversarial network (GAN). Then partial links of the initial-triggers are selected to form a trigger set, according to the gradient information of the attack discriminator in the GAN, so as to reduce the size of triggers and improve the concealment of the attack. Experimental results show that Dyn-Backdoor launches successful backdoor attacks on the state-of-the-art DLP models with success rate more than 90%. Additionally, we conduct a possible defense against Dyn-Backdoor to testify its resistance in defensive settings, highlighting the needs of defenses for backdoor attacks on DLP.

Salient Feature Extractor for Adversarial Defense on Deep Neural Networks

Recent years have witnessed unprecedented success achieved by deep learning models in the field of computer vision. However, their vulnerability towards carefully crafted adversarial examples has also attracted the increasing attention of researchers. Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature(SF) and trivial feature(TF). The former represents the class-related feature, while the latter is usually adopted to mislead the model. We extract these two features with coupled generative adversarial network model and put forward a novel detection and defense method named salient feature extractor (SFE) to defend against adversarial attacks. Concretely, detection is realized by separating and comparing the difference between SF and TF of the input. At the same time, correct labels are obtained by re-identifying SF to reach the purpose of defense. Extensive experiments are carried out on MNIST, CIFAR-10, and ImageNet datasets where SFE shows state-of-the-art results in effectiveness and efficiency compared with baselines. Furthermore, we provide an interpretable understanding of the defense and detection process.

DeepPoison: Feature Transfer Based Stealthy Poisoning Attack

Deep neural networks are susceptible to poisoning attacks by purposely polluted training data with specific triggers. As existing episodes mainly focused on attack success rate with patch-based samples, defense algorithms can easily detect these poisoning samples. We propose DeepPoison, a novel adversarial network of one generator and two discriminators, to address this problem. Specifically, the generator automatically extracts the target class' hidden features and embeds them into benign training samples. One discriminator controls the ratio of the poisoning perturbation. The other discriminator works as the target model to testify the poisoning effects. The novelty of DeepPoison lies in that the generated poisoned training samples are indistinguishable from the benign ones by both defensive methods and manual visual inspection, and even benign test samples can achieve the attack. Extensive experiments have shown that DeepPoison can achieve a state-of-the-art attack success rate, as high as 91.74%, with only 7% poisoned samples on publicly available datasets LFW and CASIA. Furthermore, we have experimented with high-performance defense algorithms such as autodecoder defense and DBSCAN cluster detection and showed the resilience of DeepPoison.

ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries

With the successful application of deep learning models in many real-world tasks, the model robustness becomes more and more critical. Often, we evaluate the robustness of the deep models by attacking them with purposely generated adversarial samples, which is computationally costly and dependent on the specific attackers and the model types. This work proposes a generic evaluation metric ROBY, a novel attack-independent robustness measure based on the model's decision boundaries. Independent of adversarial samples, ROBY uses the inter-class and intra-class statistic features to capture the features of the model's decision boundaries. We experimented on ten state-of-the-art deep models and showed that ROBY matches the robustness gold standard of attack success rate (ASR) by a strong first-order generic attacker. with only 1% of time cost. To the best of our knowledge, ROBY is the first lightweight attack-independent robustness evaluation metric that can be applied to a wide range of deep models. The code of ROBY is open sourced at https://github.com/baaaad/ROBY-Evaluating-the-Robustness-of-a-Deep-Model-by-its-Decision-Boundaries.

FineFool: Fine Object Contour Attack via Attention

Machine learning models have been shown vulnerable to adversarial attacks launched by adversarial examples which are carefully crafted by attacker to defeat classifiers. Deep learning models cannot escape the attack either. Most of adversarial attack methods are focused on success rate or perturbations size, while we are more interested in the relationship between adversarial perturbation and the image itself. In this paper, we put forward a novel adversarial attack based on contour, named FineFool. Finefool not only has better attack performance compared with other state-of-art white-box attacks in aspect of higher attack success rate and smaller perturbation, but also capable of visualization the optimal adversarial perturbation via attention on object contour. To the best of our knowledge, Finefool is for the first time combines the critical feature of the original clean image with the optimal perturbations in a visible manner. Inspired by the correlations between adversarial perturbations and object contour, slighter perturbations is produced via focusing on object contour features, which is more imperceptible and difficult to be defended, especially network add-on defense methods with the trade-off between perturbations filtering and contour feature loss. Compared with existing state-of-art attacks, extensive experiments are conducted to show that Finefool is capable of efficient attack against defensive deep models.