Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represents a further serious threat undermining the dependability of AI techniques. In a backdoor attack, the attacker corrupts the training data so to induce an erroneous behaviour at test time. Test time errors, however, are activated only in the presence of a triggering event corresponding to a properly crafted input sample. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. In the last few years, backdoor attacks have been the subject of an intense research activity focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview paper is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. As such, the proposed analysis is particularly suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in. 

/pdf/an-overview-of-backdoor-attacks-against-deep-neural-networks-ebuj5aqx.pdf

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Generative Adversarial Network (GAN) models are nowadays able to generate synthetic images which are visually indistinguishable from the real ones, thus raising serious concerns about the spread of fake news and the need to develop tools to distinguish fake and real images in order to preserve the trustworthiness of digital images. The most powerful current detection methods are based on Deep Learning (DL) technology. While these methods get excellent performance when tested under conditions similar to those considered for training, they often suffer from a lack of robustness and generalization ability, as they fail to detect fake images that are generated by “unseen” GAN models. A possibility to overcome this problem is to develop tools that rely on the semantic attributes of the image. In this paper, we propose a semantic-based method for distinguishing GAN-generated from real faces, that relies on the analysis of inter-eye symmetries and inconsistencies. The method resorts to the superior capabilities of similarity learning of extracting representative and robust features. More specifically, a Siamese Neural Network (SNN) is utilized to extract high-level features characterizing the inter-eye similarity, that can be used to discriminate between real and synthetic pairs of eyes. We carried out extensive experiments to assess the performance of the proposed method in both matched and mismatched conditions pertaining to the GAN type used to generate the synthetic images and the robustness of the method in presence of post-processing. The results we got are comparable, and in some cases superior, to those achieved by the best performing state-of-the-art method leveraging on the analysis of the entire face image.

An Eyes-Based Siamese Neural Network for the Detection of GAN-Generated Face Images

Universal backdoor attack on deep neural networks for malware detection

We propose a stealthy clean-label video backdoor attack against Deep Learning (DL)-based models aiming at detecting a particular class of spoofing attacks, namely video rebroadcast attacks. The injected backdoor does not affect spoofing detection in normal conditions, but induces a misclassification in the presence of a specific triggering signal. The proposed backdoor relies on a temporal trigger altering the average chrominance of the video sequence. The backdoor signal is designed by taking into account the peculiarities of the Human Visual System (HVS) to reduce the visibility of the trigger, thus increasing the stealthiness of the backdoor. To force the network to look at the presence of the trigger in the challenging clean-label scenario, we choose the poisoned samples used for the injection of the backdoor following a so-called Outlier Poisoning Strategy (OPS). According to OPS, the triggering signal is inserted in the training samples that the network finds more difficult to classify. The effectiveness of the proposed backdoor attack and its generality are validated experimentally on different datasets and anti-spoofing rebroadcast detection architectures. 

/pdf/a-temporal-chrominance-trigger-for-clean-label-backdoor-262ll8ik.pdf

A Temporal Chrominance Trigger for Clean-Label Backdoor Attack Against Anti-Spoof Rebroadcast Detection

Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research. Training generative adversarial neural networks (GAN) usually requires large amounts of training data. Federated learning (FL) provides a way of training a central model using distributed data from different medical institutions while keeping raw data locally. However, FL is vulnerable to backdoor attack, an adversarial by poisoning training data, given the central server cannot access the original data directly. Most backdoor attack strategies focus on classification models and centralized domains. In this study, we propose a way of attacking federated GAN (FedGAN) by treating the discriminator with a commonly used data poisoning strategy in backdoor attack classification models. We demonstrate that adding a small trigger with size less than 0.5% of the original image size can corrupt the FedGAN model. Based on the proposed attack, we provide two effective defense strategies: global malicious detection and local training regularization. We show that combining the two defense strategies yields a robust medical image generation. 

/pdf/backdoor-attack-is-a-devil-in-federated-gan-based-medical-13o3012b.pdf

Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis

A Master Key backdoor for universal impersonation attack against DNN-based face verification

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represents a further serious threat undermining the dependability of AI techniques. In a backdoor attack, the attacker corrupts the training data so to induce an erroneous behaviour at test time. Test time errors, however, are activated only in the presence of a triggering event corresponding to a properly crafted input sample. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. In the last few years, backdoor attacks have been the subject of an intense research activity focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview paper is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. As such, the proposed analysis is particularly suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in.

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences.

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A Master Key backdoor for universal impersonation attack against DNN-based face verification

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences.