The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be a...
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The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be activated to manipulate the model’s inference. Backdoor attacks can lead to severe consequences, such as misidentifying traffic signs during autonomous driving, posing a risk of causing traffic accidents. Recently, there has been a gradual evolution of frequency-domain backdoor attacks. However, since the change of both amplitude and its corresponding phase will significantly affect image appearance, most of the existing frequency-domain backdoor attacks change only the amplitude, which results in a suboptimal efficacy of the attack. In this work, we propose an attack called IBAQ, to solve this problem by blurring semantic information of the trigger image through the quadratic phase. Initially, we convert the trigger and benign sample to YCrCb space. Then, we perform the fast Fourier transform on the Y channel, blending the trigger image’s amplitude and quadratic phase linearly with the benign sample’s amplitude and phase. IBAQ achieves covert injection of trigger information within amplitude and phase, enhancing the attack effect. We validate the effectiveness and stealthiness of IBAQ through comprehensive experiments.
This book aims to provide a comprehensive understanding of tensor computation and its applications in seismic data analysis, exclusively catering to seasoned researchers, graduate students, and industrial engineers al...
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ISBN:
(数字)9783031789007
ISBN:
(纸本)9783031788994;9783031789021
This book aims to provide a comprehensive understanding of tensor computation and its applications in seismic data analysis, exclusively catering to seasoned researchers, graduate students, and industrial engineers alike. Tensor emerges as a natural representation of multi-dimensional modern seismic data, and tensor computation can help prevent possible harm to the multi-dimensional geological structure of the subsurface that occurred in classical seismic data analysis.
It delivers a wealth of theoretical, computational, technical, and experimental details, presenting an engineer's perspective on tensor computation and an extensive investigation of tensor-based seismic data analysis techniques. Embark on a transformative exploration of seismic data processing—unlock the potential of tensor computation and reshape your approach to high-dimensional geological structures.
The discussion begins with foundational chapters, providing a solid background in both seismic data processing and tensor computation. The heart of the book lies in its seven chapters on tensor-based seismic data analysis methods. From structured low-tubal-rank tensor completion to cutting-edge techniques like tensor deep learning and tensor convolutional neural networks, each method is meticulously detailed. The superiority of tensor-based data analysis methods over traditional matrix-based data analysis approaches is substantiated through synthetic and real field examples, showcasing their prowess in handling high-dimensional modern seismic data. Notable chapters delve into seismic noise suppression, seismic data interpolation, and seismic data super-resolution using advanced tensor models. The final chapter provides a cohesive summary of the conclusion and future research directions, ensuring readers facilitate a thorough understanding of tensor computation applications in seismic data processing. The appendix includes a hatful of information on existing tensor computation software, enhancing the b
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