We propose a novel framework for crowdsourced images to determine the
likelihood of an image being fake. We use a service-oriented approach to model
and represent crowdsourced images uploaded on social media, as image services.
Trust may, in some circumstances, be determined by using only the
non-functional attributes of an image service, i.e., image metadata. We define
intention of changes as a key parameter to ascertain fake image services. A
novel framework is proposed to estimate the intention of underlying changes
considering change in semantics of an image. Our experiments show high accuracy
using a large real dataset.

通过服务导向的方法，我们针对众包图像提出了一个新的框架来确定图像是否为伪造，其中使用图像的非功能性属性来识别图像服务的可信度。我们通过改变图像的语义来估计底层变化的意图，并通过实验表明，在一个大规模的真实数据集上具有很高的准确性。

确定虚假众包图像服务变更的意图

Determining Intent of Changes to Ascertain Fake Crowdsourced Image  Services

Image annotation for active learning is labor-intensive. Various automatic
and semi-automatic labeling methods are proposed to save the labeling cost, but
a reduction in the number of labeled instances does not guarantee a reduction
in cost because the queries that are most valuable to the learner may be the
most difficult or ambiguous cases, and therefore the most expensive for an
oracle to label accurately. In this paper, we try to solve this problem by
using image metadata to offer the oracle more clues about the image during
annotation process. We propose a Context Aware Image Annotation Framework
(CAIAF) that uses image metadata as similarity metric to cluster images into
groups for annotation. We also present useful metadata information as context
for each image on the annotation interface. Experiments show that it reduces
that annotation cost with CAIAF compared to the conventional framework, while
maintaining a high classification performance.

本论文提出了一种上下文感知图像注释框架（CAIAF），利用图像元数据作为相似性度量将图像聚类成组进行注释，并在注释界面上为每张图像提供有用的元数据信息作为上下文，实验结果表明它相对于传统框架减少了注释成本，同时保持了高分类性能。