As AI-generated image (AIGI) methods become more powerful and accessible, it
has become a critical task to determine if an image is real or AI-generated.
Because AIGI lack the signatures of photographs and have their own unique
patterns, new models are needed to determine if an image is AI-generated. In
this paper, we investigate the ability of the Contrastive Language-Image
Pre-training (CLIP) architecture, pre-trained on massive internet-scale data
sets, to perform this differentiation. We fine-tune CLIP on real images and
AIGI from several generative models, enabling CLIP to determine if an image is
AI-generated and, if so, determine what generation method was used to create
it. We show that the fine-tuned CLIP architecture is able to differentiate AIGI
as well or better than models whose architecture is specifically designed to
detect AIGI. Our method will significantly increase access to AIGI-detecting
tools and reduce the negative effects of AIGI on society, as our CLIP
fine-tuning procedures require no architecture changes from publicly available
model repositories and consume significantly less GPU resources than other AIGI
detection models.

通过对大规模互联网数据集进行预训练后，在真实图像和人工智能生成图像进行微调，我们展示了细调后的 Contrastive Language-Image Pre-training（CLIP）架构能够与专门设计用于检测人工智能生成图像的模型相媲美甚至更好地区分人工智能生成图像，并且能够确定生成方法。我们的方法将显著提高人工智能生成图像检测工具的可用性，减少人工智能生成图像对社会的负面影响，因为我们的 CLIP 微调过程不需要对公开可用模型仓库进行架构更改，而且消耗的 GPU 资源明显更少。

通过 CLIP 检测人工智能生成的图像

Detecting AI-Generated Images via CLIP

The tremendous hype around autonomous driving is eagerly calling for emerging
and novel technologies to support advanced mobility use cases. As car
manufactures keep developing SAE level 3+ systems to improve the safety and
comfort of passengers, traffic authorities need to establish new procedures to
manage the transition from human-driven to fully-autonomous vehicles while
providing a feedback-loop mechanism to fine-tune envisioned autonomous systems.
Thus, a way to automatically profile autonomous vehicles and differentiate
those from human-driven ones is a must. In this paper, we present a
fully-fledged framework that monitors active vehicles using camera images and
state information in order to determine whether vehicles are autonomous,
without requiring any active notification from the vehicles themselves.
Essentially, it builds on the cooperation among vehicles, which share their
data acquired on the road feeding a machine learning model to identify
autonomous cars. We extensively tested our solution and created the NexusStreet
dataset, by means of the CARLA simulator, employing an autonomous driving
control agent and a steering wheel maneuvered by licensed drivers. Experiments
show it is possible to discriminate the two behaviors by analyzing video clips
with an accuracy of 80%, which improves up to 93% when the target state
information is available. Lastly, we deliberately degraded the state to observe
how the framework performs under non-ideal data collection conditions.

利用图像和状态信息，该研究提出了一种全面的自动驾驶监测框架，旨在自动识别自动驾驶车辆与人工驾驶车辆的行为，并在充分考虑数据采集条件不佳的情况下进行测试。

行为分析检测自主驾驶车辆

Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis

When performing inference on probabilistic models, target densities often
become intractable, necessitating the use of Monte Carlo samplers. We develop a
methodology for unbiased differentiation of the Metropolis-Hastings sampler,
allowing us to differentiate through probabilistic inference. By fusing recent
advances in stochastic differentiation with Markov chain coupling schemes, the
procedure can be made unbiased, low-variance, and automatic. This allows us to
apply gradient-based optimization to objectives expressed as expectations over
intractable target densities. We demonstrate our approach by finding an
ambiguous observation in a Gaussian mixture model and by maximizing the
specific heat in an Ising model.

本文提出了一种用于无偏区别 Metropolis-Hastings 采样器的方法，使得我们能够通过概率推理进行不可行目标密度的优化，该方法通过融合随机区分的最新进展和马尔可夫链耦合方案，可以使该过程无偏、低方差和自动化，然后将其应用于期望为不可行目标密度的优化中。在文中所提出的方法中，分别应用到高斯混合模型中找到不明显的观察和 Ising 模型中的特定热容的最大化。

Metropolis-Hastings 微分优化难以计算的密度函数

Differentiating Metropolis-Hastings to Optimize Intractable Densities

We present a very simple algorithm for attention that requires $O(1)$ memory
with respect to sequence length and an extension to self-attention that
requires $O(\log n)$ memory. This is in contrast with the frequently stated
belief that self-attention requires $O(n^2)$ memory. While the time complexity
is still $O(n^2)$, device memory rather than compute capability is often the
limiting factor on modern accelerators. Thus, reducing the memory requirements
of attention allows processing of longer sequences than might otherwise be
feasible. We provide a practical implementation for accelerators that requires
$O(\sqrt{n})$ memory, is numerically stable, and is within a few percent of the
runtime of the standard implementation of attention. We also demonstrate how to
differentiate the function while remaining memory-efficient. For sequence
length 16384, the memory overhead of self-attention is reduced by 59X for
inference and by 32X for differentiation.

提出一种对 attention 算法的简化，使其在处理长序列时更省内存，并对其进行扩展，以此降低 self-attention 的内存消耗，同时保证数值稳定性和可微分性，通过测试结果表明，这样可以让 self-attention 在推断和不同阶段的内存开销分别降低 59 倍和 32 倍。