Diffusion-based text-to-image (T2I) models generate high-fidelity images for
given textual prompts. They are trained on large datasets scraped from the
Internet, potentially containing unacceptable concepts (e.g., copyright
infringing or unsafe). Retraining T2I models after filtering out unacceptable
concepts in the training data is inefficient and degrades utility. Hence, there
is a need for concept removal techniques (CRTs) which are effective in removing
unacceptable concepts, utility-preserving on acceptable concepts, and robust
against evasion with adversarial prompts. None of the prior filtering and
fine-tuning CRTs satisfy all these requirements simultaneously.
We introduce Espresso, the first robust concept filter based on Contrastive
Language-Image Pre-Training (CLIP). It identifies unacceptable concepts by
projecting the generated image's embedding onto the vector connecting
unacceptable and acceptable concepts in the joint text-image embedding space.
This ensures robustness by restricting the adversary to adding noise only along
this vector, in the direction of the acceptable concept. Further fine-tuning
Espresso to separate embeddings of acceptable and unacceptable concepts, while
preserving their pairing with image embeddings, ensures both effectiveness and
utility. We evaluate Espresso on eleven concepts to show that it is effective
(~5% CLIP accuracy on unacceptable concepts), utility-preserving (~93%
normalized CLIP score on acceptable concepts), and robust (~4% CLIP accuracy on
adversarial prompts for unacceptable concepts). Finally, we present theoretical
bounds for the certified robustness of Espresso against adversarial prompts,
and an empirical analysis.

基于对比性语言图像预训练 (CLIP) 方法的 Espresso 模型，能有效去除不可接受的概念，保持可接受概念的有用性，并对抗敌对提醒，同时具有强大的鲁棒性。

Espresso：文本到图像模型中的鲁棒概念过滤

Espresso: Robust Concept Filtering in Text-to-Image Models

Contrastive Language-Image Pre-training (CLIP) models have demonstrated
remarkable generalization capabilities across multiple challenging distribution
shifts. However, there is still much to be explored in terms of their
robustness to the variations of specific visual factors. In real-world
applications, reliable and safe systems must consider other safety objectives
beyond classification accuracy, such as predictive uncertainty. Yet, the
effectiveness of CLIP models on such safety-related features is less-explored.
Driven by the above, this work comprehensively investigates the safety
objectives of CLIP models, specifically focusing on three key properties:
resilience to visual factor variations, calibrated uncertainty estimations, and
the ability to detect anomalous inputs. To this end, we study 83 CLIP models
and 127 ImageNet classifiers. They are diverse in architecture, (pre)training
distribution and training strategies. We consider 10 visual factors (e.g.,
shape and pattern), 5 types of out-of-distribution data, and 8 natural and
challenging test conditions with different shift types, such as texture, style,
and perturbation shifts. Our study has unveiled several previously unknown
insights into CLIP models. For instance, they are not consistently more
calibrated than other ImageNet models, which contradicts existing findings.
Additionally, our analysis underscores the significance of training source
design by showcasing its profound influence on the three safety-related
properties. We believe our comprehensive study can shed light on and help guide
the development of more robust and reliable CLIP models.

该研究全面调查了 Contrastive Language-Image Pre-training (CLIP) 模型的安全目标，特别关注三个关键属性：对视觉因素变化的弹性，校准的不确定性估计以及检测异常输入的能力。研究揭示了 CLIP 模型的一些以前未知的见解，并强调了训练源设计的重要性及其对三个安全相关属性的深远影响。该全面研究有助于引导更加稳健可靠的 CLIP 模型的发展。

深入研究对比语言图像预训练（CLIP）的鲁棒性

A Closer Look at the Robustness of Contrastive Language-Image  Pre-Training (CLIP)

Recognizing objects from sparse and noisy events becomes extremely difficult
when paired images and category labels do not exist. In this paper, we study
label-free event-based object recognition where category labels and paired
images are not available. To this end, we propose a joint formulation of object
recognition and image reconstruction in a complementary manner. Our method
first reconstructs images from events and performs object recognition through
Contrastive Language-Image Pre-training (CLIP), enabling better recognition
through a rich context of images. Since the category information is essential
in reconstructing images, we propose category-guided attraction loss and
category-agnostic repulsion loss to bridge the textual features of predicted
categories and the visual features of reconstructed images using CLIP.
Moreover, we introduce a reliable data sampling strategy and local-global
reconstruction consistency to boost joint learning of two tasks. To enhance the
accuracy of prediction and quality of reconstruction, we also propose a
prototype-based approach using unpaired images. Extensive experiments
demonstrate the superiority of our method and its extensibility for zero-shot
object recognition. Our project code is available at
https://github.com/Chohoonhee/Ev-LaFOR.

该研究论文探索了无标签的事件感知物体识别，提出了一种结合图像重构和对象识别的方法，通过 Contrastive Language-Image Pre-training (CLIP) 从事件中重构图像，并借助分类信息提升两者之间的关联性，最后通过无配对图像的基于原型的方法增强预测准确度和重构质量，实现了零样本物体识别。