Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to create a targeted attack on a deep model that outputs a label of attacker'

Keyphrases: label universal targeted attack images

Dataset description:
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
HQ-WMCA: The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database consists of 2904 short multi-modal video recordings of both bona-fide and presentation attacks. There are 555 bonafide presentations from 51 participants and the remaining 2349 are presentation attacks.
MSU-MFSD: The MSU-MFSD dataset contains 280 video recordings of genuine and attack faces. 35 individuals have participated in the development of this database with a total of 280 videos.
APRICOT: APRICOT is a collection of over 1,000 annotated photographs of printed adversarial patches in public locations. The patches target several object categories for three COCO-trained detection models, and the photos represent natural variation in position, distance, lighting conditions, and viewing angle.

Options:- A: ImageNet  B: HQ-WMCA  C: MSU-MFSD  D: APRICOT
ImageNet