Recent research has shown that deep neural networks are vulnerable to backdoor attacks. A carefully-designed backdoor trigger will mislead the victim model to misclassify any sample with the trigger to the target label. Nevertheless, existing works usually utilize visible triggers, such as a white square at the corner of the image, which are easily detected by human inspections. Current efforts on developing invisible triggers yield low attack success in the physical domain. In this paper, we propose Kaleidoscope, an RGB (red, green, and blue) filter-based backdoor attack method, which utilizes RGB filter operations as the backdoor trigger. To enhance the attack success rate, we design a novel model-dependent filter trigger generation algorithm. We also introduce two constraints in the loss function to make the backdoored samples more natural and less distorted. Extensive experiments on CIFAR-10, CIFAR-100, ImageNette, and VGG-Flower have demonstrated that RGB filter-processed samples not only achieve high attack success rate but also are unnoticeable to humans. It is shown that Kaleidoscope can reach an attack success rate of more than 84% in the physical world under different lighting intensities and shooting angles. Kaleidoscope is also shown to be robust to state-of-the-art backdoor defenses, such as spectral signature, STRIP, and MNTD. 

Kaleidoscope: Physical Backdoor Attacks against Deep Neural Networks with RGB Filters

—Deep neural networks have achieved remarkable success on a variety of mission-critical tasks. However, recent studies show that deep neural networks are vulnerable to backdoor attacks, where the attacker releases backdoored models that behave normally on benign samples but misclassify any trigger-imposed samples to a target label. Unlike adversarial examples, backdoor attacks manipulate both the inputs and the model, perturbing samples with the trigger and injecting backdoors into the model. In this paper, we propose a novel attention-based evasive backdoor attack, dubbed A TTEQ -NN . Different from existing works that arbitrarily set the trigger mask, we carefully design an attention- based trigger mask determination framework, which places the trigger at the crucial region with the most significant influence on the prediction results. To make the trigger-imposed samples appear more natural and imperceptible to human inspectors, we introduce a Quality-of-Experience (QoE) term into the loss function of trigger generation and carefully adjust the transparency of the trigger. During the process of iteratively optimizing the trigger generation and the backdoor injection components, we propose an alternating retraining strategy, which is shown to be effective in improving the clean data accuracy and evading some model-based defense approaches. We evaluate A TTEQ -NN with extensive experiments on VGG- Flower, CIFAR-10, GTSRB, CIFAR-100, and ImageNette datasets. The results show that A TTEQ -NN can increase the attack success rate by as much as 82% over baselines when the poison ratio is low while achieving a high QoE of the backdoored samples. We demonstrate that A TTEQ -NN reaches an attack success rate of more than 37.78% in the physical world under different lighting conditions and shooting angles. A TTEQ -NN preserves an attack success rate of more than 92.5% even if the original backdoored model is fine-tuned with clean data. It is shown that A TTEQ -NN is also effective in transfer learning scenarios. Our user studies show that the backdoored samples generated by A TTEQ -NN are indiscernible under visual inspections. A TTEQ -NN is shown to be evasive to state-of-the-art defense methods, including model pruning, NAD, STRIP, NC, and MNTD. We will open-source our codes upon publication.

/pdf/atteq-nn-attention-based-qoe-aware-evasive-backdoor-attacks-19pb6zse.pdf

ATTEQ-NN: Attention-based QoE-aware Evasive Backdoor Attacks

LIGAA: Generative adversarial attack method based on low-frequency information

Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this paper, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. It contains two novel components. First, it uses a novel saliency map decoder to learn the importance of speech samples to the decision of a targeted speaker identification system, so as to make the attacker focus on generating artificial noise to the important samples. It also proposes an angular loss function to push the speaker embedding far away from the source speaker. Our experimental results demonstrate that the proposed SSED yields the state-of-the-art performance, i.e. over 97% targeted attack success rate and a signal-to-noise level of over 39 dB on both the open-set and close-set speaker identification tasks, with a low computational cost. 

/pdf/symmetric-saliency-based-adversarial-attack-to-speaker-2w5v8qe3.pdf

Symmetric Saliency-Based Adversarial Attack to Speaker Identification

The majority of methods for crafting adversarial attacks have focused on scenes with a single dominant object (e.g., images from ImageNet). On the other hand, natural scenes include multiple dominant objects that are semantically related. Thus, it is crucial to explore designing attack strategies that look beyond learning on single-object scenes or attack single-object victim classifiers. Due to their inherent property of strong transferability of perturbations to unknown models, this paper presents the first approach of using generative models for adversarial attacks on multi-object scenes. In order to represent the relationships between different objects in the input scene, we leverage upon the open-sourced pre-trained vision-language model CLIP (Contrastive Language-Image Pre-training), with the motivation to exploit the encoded semantics in the language space along with the visual space. We call this attack approach Generative Adversarial Multi-object scene Attacks (GAMA). GAMA demonstrates the utility of the CLIP model as an attacker's tool to train formidable perturbation generators for multi-object scenes. Using the joint image-text features to train the generator, we show that GAMA can craft potent transferable perturbations in order to fool victim classifiers in various attack settings. For example, GAMA triggers ~16% more misclassification than state-of-the-art generative approaches in black-box settings where both the classifier architecture and data distribution of the attacker are different from the victim. Our code is available here: https://abhishekaich27.github.io/gama.html

GAMA: Generative Adversarial Multi-Object Scene Attacks

The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of the existing works for adversarial attack are gradient-based and suf-fer from the latency efficiencies and the load on GPU memory. Thegenerative-based adversarial attacks can get rid of this limitation,and some relative works propose the approaches based on GAN.However, suffering from the difficulty of the convergence of train-ing a GAN, the adversarial examples have either bad attack abilityor bad visual quality. In this work, we find that the discriminatorcould be not necessary for generative-based adversarial attack, andpropose theSymmetric Saliency-based Auto-Encoder (SSAE)to generate the perturbations, which is composed of the saliencymap module and the angle-norm disentanglement of the featuresmodule. The advantage of our proposed method lies in that it is notdepending on discriminator, and uses the generative saliency map to pay more attention to label-relevant regions. The extensive exper-iments among the various tasks, datasets, and models demonstratethat the adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.The code is available at this https URL.

/pdf/discriminator-free-generative-adversarial-attack-1kpb4ao0w6.pdf

Discriminator-Free Generative Adversarial Attack

Cooperative multi-agent reinforcement learning (cMARL) has many real applications, but the policy trained by existing cMARL algorithms is not robust enough when deployed. There exist also many methods about adversarial attacks on the RL system, which implies that the RL system can suffer from adversarial attacks, but most of them focused on single agent RL. In this paper, we propose a \textit{sparse adversarial attack} on cMARL systems. We use (MA)RL with regularization to train the attack policy. Our experiments show that the policy trained by the current cMARL algorithm can obtain poor performance when only one or a few agents in the team (e.g., 1 of 8 or 5 of 25) were attacked at a few timesteps (e.g., attack 3 of total 40 timesteps).

Sparse Adversarial Attack in Multi-agent Reinforcement Learning

The Deep Neural Networks are vulnerable to adversarial examples (Figure 1), making the DNNs-based systems collapsed by adding the inconspicuous perturbations to the images. Most of the existing works for adversarial attack are gradient-based and suffer from the latency efficiencies and the load on GPU memory. The generative-based adversarial attacks can get rid of this limitation, and some relative works propose the approaches based on GAN. However, suffering from the difficulty of the convergence of training a GAN, the adversarial examples have either bad attack ability or bad visual quality. In this work, we find that the discriminator could be not necessary for generative-based adversarial attack, and propose the Symmetric Saliency-based Auto-Encoder (SSAE) to generate the perturbations, which is composed of the saliency map module and the angle-norm disentanglement of the features module. The advantage of our proposed method lies in that it is not depending on discriminator, and uses the generative saliency map to pay more attention to label-relevant regions. The extensive experiments among the various tasks, datasets, and models demonstrate that the adversarial examples generated by SSAE not only make the widely-used models collapse, but also achieves good visual quality. The code is available at: https://github.com/BravoLu/SSAE.

pdf/discriminator-free-generative-adversarial-attack-1qgovcrcd4.pdf

Discriminator-free Generative Adversarial Attack

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Discriminator-Free Generative Adversarial Attack

Sparse Adversarial Attack in Multi-agent Reinforcement Learning

Discriminator-free Generative Adversarial Attack