DeepKeep’s Post

Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this letter, a practical #threat #model which utilizes #adversarial #attacks for anti-eavesdropping is proposed and a physical #intra-#class #universal #adversarial #perturbation (#IC-#UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, the authors develop a #physical #attack #algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. ---- Ruiqi Li, Hongshu Liao, Jiancheng An, Chau Yuen, Lu Gan More details can be found at this link: https://lnkd.in/gPSEkGnS

Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this letter, a practical #threat #model which utilizes #adversarial #attacks for anti-eavesdropping is proposed and a physical #intra-#class #universal #adversarial #perturbation (#IC-#UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, the authors develop a #physical #attack #algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. ---- Ruiqi Li, Hongshu Liao, Jiancheng An, Chau Yuen, Lu Gan More details can be found at this link: https://lnkd.in/gPSEkGnS

Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this letter, a practical #threat #model which utilizes #adversarial #attacks for anti-eavesdropping is proposed and a physical #intra-#class #universal #adversarial #perturbation (#IC-#UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, the authors develop a #physical #attack #algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. ---- @Ruiqi Li, @Hongshu Liao, Jiancheng An, Chau Yuen, @Lu Gan More details can be found at this link: https://lnkd.in/gPSEkGnS

Low- Power Image Classification with the BrainChip Akida 1000 https://lnkd.in/enFaeY_Y

New tutorial on image classification using Ultralytics HUB 🔥🔥🔥 Image classification uses machine learning algorithms to categorize images, employing neural networks for pattern recognition from visual data. Ultralytics HUB, developed by the creators of YOLOv5 and YOLOv8, simplifies data visualization, AI model training, and real-world deployment. It supports image classification with deep learning algorithms and neural networks, playing a role in advancing AI. In this video, Nicolai Nielsen will guide you through the steps of image classification using HUB. What's Included 😍 ✅ Overview of image classification ✅ Finetuning the image classification model on custom data using Ultralytics HUB ✅ Custom dataset overview (brain tumor) ✅ Overview of Ultralytics HUB Cloud training process ✅ Training metrics, deployment, and export process insights Watch now 👇 https://lnkd.in/df8_8br9 #computervision #youtubetutorial #yolov8 #imageclassification #aiandml

Check out: I, Cyborg: Using Co-Intelligence

5 Best Deepfake Detector Tools & Techniques

🌟 Publication Alert! 🌟 I'm excited to share that my research paper titled "Evaluating the Effectiveness of Attacks and Defenses on Machine Learning Through Adversarial Samples" has been published! This study dives deep into the dynamics between adversarial attacks and defenses, particularly focusing on the Carlini & Wagner attack and KDE defense. 🔑 Key Insights: - Analysis of the effectiveness of adversarial attacks and defenses across various parameter settings. - Exploration of the optimal parameter values for both attacks and defenses. - Discussion on the necessary trade-offs to enhance the effectiveness of these methods. I believe, by understanding the interplay of different parameters, we can better prepare our models to withstand adversarial threats. https://lnkd.in/dVmn4r2V I invite everyone to read the paper and your thoughts on how we can continue to fortify AI systems against evolving threats. #AdversarialMachineLearning #AdversarialAttacks #AdversarialDefenses #RobustnessAssessment #MachineLearning #SecurityTesting #AI #NeuralNetworks #Research #Publication

🌟 Exciting News! 🌟 I'm so happy to share that I have joined Ready Tensor, Inc. to help drive outreach and spread the word about this incredible platform for data science, machine learning, and AI! My passion for AI, machine learning, and data science combined with a deep love for knowledge-sharing through technical writing has led me to this fantastic opportunity. Ready Tensor, Inc. is a hub for sharing data science projects, publications, and thought-provoking articles on NLP and AI, making it the perfect fit for enthusiasts, researchers, and professionals looking to grow and connect. I’m looking forward to sharing insightful content with you all soon, so stay tuned! If you’d like to know more about Ready Tensor and what it has to offer, feel free to check out the website (https://lnkd.in/gESF4Pgc) or reach out. Let’s connect and spread the word about the power of AI and data science! 🚀 #AI #DataScience #MachineLearning #ReadyTensor #NLP #KnowledgeSharing #publications #article

🌟 Excited to announce our latest blog post on "ONNXPruner: ONNX-Based General Model Pruning Adapter" (arXiv:2404.08016v1). We delve into the challenges faced in applying model pruning algorithms across platforms and introduce ONNXPruner, a versatile pruning adapter designed for ONNX format models. With its use of node association trees and a tree-level evaluation method, ONNXPruner demonstrates strong adaptability and increased efficacy across diverse deep learning frameworks and hardware platforms. Check out the full post for insights into advancing the practical application of model pruning: https://bit.ly/3Jk2c1u #MachineLearning #DeepLearning #ONNX #ModelPruning