Detecting Backdoors with Meta-Models

It is widely known that it is possible to implant backdoors into neural networks, by which an attacker can choose an input to produce a particular undesirable output (e.g. misclassify an image). We propose to use meta-models, neural networks that take another network's parameters as input, to detect backdoors directly from model weights. To this end we present a meta-model architecture and train it on a dataset of ~4000 clean and backdoored CNNs trained on CIFAR-10. Our approach is simple and scalable, and is able to detect the presence of a backdoor with accuracy when the test trigger pattern is i.i.d., with some success even on out-of-distribution backdoors.

VQGAN-CLIP: Open domain image generation and editing

Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models. We demonstrate a novel methodology for both tasks which is capable of producing images of high visual quality from text prompts of significant semantic complexity without any training by using a multimodal encoder to guide image generations. We demonstrate on a variety of tasks how using CLIP [37] to guide VQGAN [11] produces higher visual quality outputs than prior, less flexible approaches like DALL-E [38], GLIDE [33] and Open-Edit [24], despite not being trained for the tasks presented. Our code is available in a public repository.