Diffusion modelRobustnessIn recent years, deep learning has seen widespread application in many fields. However, the emergence of adversarial examples has revealed a critical vulnerability, making them susceptible to potential attacks. Although numerous adversarial attack methods have been proposed, they ...
主要就是计算模型预测噪声与实际噪声之间的损失,用来训练去噪模型 denoise_model。 # 定义损失函数 p_losses,有三种损失类型:L1、L2 和 Huber,默认使用 L1 损失 def p_losses(denoise_model, x_start, t, noise=None, loss_type="l1"): # 如果没有提供噪声,则随机生成一个与 x_start 形状相同的噪声 if ...
Diffusion models are trained to create new images by learning how to reverse the process of adding noise to data. The training process begins with forward diffusion: showing the model many examples of images at different noise levels until the image is all noise. This type of random noise is...
Perceptual Image Compression Model Basis and Objective: Previous Work: The model is based on prior research and combines a perceptual loss and a patch-based adversarial objective. Purpose: To ensure that reconstructions are confined to the image manifold, enforcing local realism and avoiding the blurr...
We introduce the concept of deceptive diffusion鈥攖raining a generative AI model to produce adversarial images. Whereas a traditional adversarial attack algorithm aims to perturb an existing image to induce a misclassificaton, the deceptive diffusionmodel can create an arbitrary number of new, mis...
Here are some examples of image denoising techniques that use deep learning: TNRD: The trainable nonlinear reaction-diffusion model (TNRD) is a deep neural network that performs denoising by simulating the diffusion of noise in an image using a non linear, feed-forward architecture. FC-AIDE: FC...
3.3. Model overviews 3.3.1. Vanilla generative adversarial networks Generative Adversarial Networks depend on an adversarial approach to generate new data. The two neural networks that GANs are composed of, the generator and the discriminator, work against each other. The generator attempts to generate...
DDP: Diffusion Model for Dense Visual Prediction Yuanfeng Ji1∗, Zhe Chen3∗, Enze Xie2†, Lanqing Hong2, Xihui Liu1, Zhaoqiang Liu2, Tong Lu3, Zhenguo Li2, Ping Luo1,4 1The University of Hong Kong 2Huawei Noah's Ark Lab 3Nanjing University 4Shanghai AI Laboratory https:/...
White said that diffusion models are the current go-to for image generation. They are the base model for popular image generation services, such asDall-E2, Stable Diffusion, Midjourney and Imagen. They are also used in pipelines to generate voices, video and 3D content. In addition, the...
In this work, we propose a novel Adversarial Diffusion Bridge Model, termed ADBM. ADBM directly constructs a reverse bridge from the diffused adversarial data back to its original clean examples, enhancing the purification capabilities of the original diffusion models. Through theoretical analysis and...