Trained on massive amounts of human-generated content, AI (artificial
intelligence) image synthesis is capable of reproducing semantically coherent
images that match the visual appearance of its training data. We show that when
retrained on even small amounts of their own creation, these generative-AI
models produce highly distorted images. We also show that this distortion
extends beyond the text prompts used in retraining, and that once poisoned, the
models struggle to fully heal even after retraining on only real images.

通过大量人工生成的内容进行训练，AI（人工智能）图像合成能够生成与其训练数据在视觉外观上相匹配的语义连贯的图像。我们展示了这些生成型 AI 模型在重新训练时即使只是重新训练少量自己生成的内容，也会产生高度畸变的图像。同时我们还展示了这种畸变不仅限于用于重新训练的文本提示，一旦受到毒害，这些模型在仅仅通过真实图像重新训练后依然难以完全恢复。

亲戚训练的生成型人工智能模型崩溃

Nepotistically Trained Generative-AI Models Collapse

Image acquisition conditions and environments can significantly affect
high-level tasks in computer vision, and the performance of most computer
vision algorithms will be limited when trained on distortion-free datasets.
Even with updates in hardware such as sensors and deep learning methods, it
will still not work in the face of variable conditions in real-world
applications. In this paper, we apply the object detector YOLOv7 to detect
distorted images from the dataset CDCOCO. Through carefully designed
optimizations including data enhancement, detection box ensemble, denoiser
ensemble, super-resolution models, and transfer learning, our model achieves
excellent performance on the CDCOCO test set. Our denoising detection model can
denoise and repair distorted images, making the model useful in a variety of
real-world scenarios and environments.

通过优化图像增强、检测框集成、去噪集成、超分辨率模型和迁移学习等方法，将 YOLOv7 目标检测器应用于 CDCOCO 数据集，实现了在不同环境中对失真图像的准确检测和修复。该去噪检测模型在各种真实场景中具有广泛的应用价值。

实景下扭曲图片的集成模型

An Ensemble Model for Distorted Images in Real Scenarios

We compare the robustness of humans and current convolutional deep neural
networks (DNNs) on object recognition under twelve different types of image
degradations. First, using three well known DNNs (ResNet-152, VGG-19,
GoogLeNet) we find the human visual system to be more robust to nearly all of
the tested image manipulations, and we observe progressively diverging
classification error-patterns between humans and DNNs when the signal gets
weaker. Secondly, we show that DNNs trained directly on distorted images
consistently surpass human performance on the exact distortion types they were
trained on, yet they display extremely poor generalisation abilities when
tested on other distortion types. For example, training on salt-and-pepper
noise does not imply robustness on uniform white noise and vice versa. Thus,
changes in the noise distribution between training and testing constitutes a
crucial challenge to deep learning vision systems that can be systematically
addressed in a lifelong machine learning approach. Our new dataset consisting
of 83K carefully measured human psychophysical trials provide a useful
reference for lifelong robustness against image degradations set by the human
visual system.

通过对三种著名的深度卷积神经网络在十二种不同的图像失真下的实验对比，研究表明，与这三种人工智能算法相比，人类视觉系统在几乎所有的测试图像处理中都更加鲁棒，其误差模式渐行渐远。本文还证明了，利用失真图像直接训练的深度学习神经网络在恰当的测试条件下，表现优于人类。然而，当被分别用于测试不同类型的失真图像时，它们的泛化能力极差，无法适应噪声分布的变化，这成为深度学习视觉系统所面临的关键挑战，可用一种终身机器学习的方法进行系统化解决。