Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper presents and interesting twist to adversarial examples: given a trained neural segmentation model, with slight changes to the input image that given a segmentation model, it is possible to change the segmentation prediction of certain classes or even individual objects without significantly changing the predictions to anything else. This is useful direction to explore and understand better.

Weaknesses: the methods utilized are relatively similar to earlier works on adversarial examples, so the novelty of the approach is not very high. However The main weakness of the work is that the demonstration comes without any meaningful quantitative comparisons.
review_title: Interesting addition to adversarial examples

final_score: rating: 7: Good paper, accept
review_content: The authors demonstrate how to build adversarial examples for pixel segmentations.

Pros:
- Authors convincingly show how they can minimally distort an image so that it is perceptually identical yet the segmentation for person is entirely obliterated.
- Authors show a single example that clearly demonstrates the effect where the person is entirely removed from the prediction. Additionally, the authors show some nice summary statistics where the person can be selectively removed quite reliably from the segmentation.
- First paper to approach adversarial example generation for pixel segmentation.

review_title: Review For Adversarial Examples For Semantic Image Segmentation

Decision: Accept