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Unveiling Multi-Attacks in Image Classification: How One Adversarial Perturbation Can Mislead Hundreds of Images

Adversarial attacks in image classification, a critical issue in AI security, involve subtle changes to images that mislead AI models into incorrect classifications. The research delves into the intricacies of these attacks, particularly focusing on multi-attacks, where a single alteration can simultaneously affect multiple images’ classifications. This phenomenon is not just a theoretical concern but…

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Salesforce Research Proposes MoonShot: A New Video Generation AI Model that Conditions Simultaneously on Multimodal Inputs of Image and Text

Artificial intelligence has always faced the issue of producing high-quality videos that smoothly integrate multimodal inputs like text and graphics. Text-to-video generation techniques now in use frequently concentrate on single-modal conditioning, using either text or images alone. The accuracy and control researchers can exert over the created films are limited by this unimodal technique, making…

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ByteDance Introduces the Diffusion Model with Perceptual Loss: A Breakthrough in Realistic AI-Generated Imagery

Diffusion models are a significant component in generative models, particularly for image generation, and these models are undergoing transformative advancements. These models, functioning by transforming noise into structured data, especially images, through a denoising process, have become increasingly important in computer vision and related fields. Their capability to convert pure noise into detailed images has…

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Gaussian Processes from Scratch. Gain a deeper understanding of Gaussian… | by Theo Wolf | Jan, 2024

Gain a deeper understanding of Gaussian processes by implementing them with only NumPy. Gaussian Processes (GPs) are an incredible class of models. There are very few Machine Learning algorithms that give you an accurate measure of uncertainty for free while still being super flexible. The problem is, GPs are conceptually really difficult to understand. Most…

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