Segment Anything Models (SAM) have made significant advancements in image
segmentation, allowing users to segment target portions of an image with a
single click (i.e., user prompt). Given its broad applications, the robustness
of SAM against adversarial attacks is a critical concern. While recent works
have explored adversarial attacks against a pre-defined prompt/click, their
threat model is not yet realistic: (1) they often assume the user-click
position is known to the attacker (point-based attack), and (2) they often
operate under a white-box setting with limited transferability. In this paper,
we propose a more practical region-level attack where attackers do not need to
know the precise user prompt. The attack remains effective as the user clicks
on any point on the target object in the image, hiding the object from SAM.
Also, by adapting a spectrum transformation method, we make the attack more
transferable under a black-box setting. Both control experiments and testing
against real-world SAM services confirm its effectiveness.

该研究论文提出了一种更实用的区域级攻击方法，攻击者不需要了解精确的用户提示，通过对图像中目标对象上的任意点进行点击，以隐藏对象并使其不受 Segment Anything Models（SAM）的干扰，同时通过谱变换方法使攻击更具可迁移性，控制实验和对真实世界中的 SAM 服务进行的测试证实了其有效性。

面向区域级攻击细分任意模型的实用方法

Practical Region-level Attack against Segment Anything Models

Text-to-audio generation (TTA) produces audio from a text description,
learning from pairs of audio samples and hand-annotated text. However,
commercializing audio generation is challenging as user-input prompts are often
under-specified when compared to text descriptions used to train TTA models. In
this work, we treat TTA models as a ``blackbox'' and address the user prompt
challenge with two key insights: (1) User prompts are generally
under-specified, leading to a large alignment gap between user prompts and
training prompts. (2) There is a distribution of audio descriptions for which
TTA models are better at generating higher quality audio, which we refer to as
``audionese''. To this end, we rewrite prompts with instruction-tuned models
and propose utilizing text-audio alignment as feedback signals via margin
ranking learning for audio improvements. On both objective and subjective human
evaluations, we observed marked improvements in both text-audio alignment and
music audio quality.

通过使用指令调整模型改写提示文本并利用文本音频对齐作为反馈信号，通过边界排序学习从而改善音频的质量和文本音频的对齐，从客观和主观的人类评估中观察到了明显的改善。

关于有条件音频生成中的开放提示挑战

On The Open Prompt Challenge In Conditional Audio Generation

In this paper, we propose the Matting Anything Model (MAM), an efficient and
versatile framework for estimating the alpha matte of any instance in an image
with flexible and interactive visual or linguistic user prompt guidance. MAM
offers several significant advantages over previous specialized image matting
networks: (i) MAM is capable of dealing with various types of image matting,
including semantic, instance, and referring image matting with only a single
model; (ii) MAM leverages the feature maps from the Segment Anything Model
(SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha
matte through iterative refinement, which has only 2.7 million trainable
parameters. (iii) By incorporating SAM, MAM simplifies the user intervention
required for the interactive use of image matting from the trimap to the box,
point, or text prompt. We evaluate the performance of MAM on various image
matting benchmarks, and the experimental results demonstrate that MAM achieves
comparable performance to the state-of-the-art specialized image matting models
under different metrics on each benchmark. Overall, MAM shows superior
generalization ability and can effectively handle various image matting tasks
with fewer parameters, making it a practical solution for unified image
matting. Our code and models are open-sourced at
this https URL

提出了 Matting Anything Model（MAM），一种高效且通用的框架，用于估计图像中任何实例的 alpha 掩模，该框架具有灵活和交互式的视觉或语言提示，可以处理各种类型的图像抠图​​，并且相比于其他专门化的图像抠图网络具有优势，并成功应用于不同的图像抠图基准测试中。