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Register a new userLive Video Comment Generation Based on Surrounding Frames and Live Comments
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Damai Dai
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Authors
Damai Dai
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In this paper, we propose the task of live comment generation. Live comments are a new form of comments on videos, which can be regarded as a mixture of comments and chats. A high-quality live comment should be not only relevant to the video, but also interactive with other users. In this work, we first construct a new dataset for live comment generation. Then, we propose a novel end-to-end model to generate the human-like live comments by referring to the video and the other users comments. Finally, we evaluate our model on the constructed dataset. Experimental results show that our method can significantly outperform the baselines.
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We analyze the claims that video recreations of shoulder surfing attacks offer a suitable alternative and a baseline, as compared to evaluation in a live setting. We recreated a subset of the factors of a prior video-simulation experiment conducted by Aviv et al. (ACSAC 2017), and model the same scenario using live participants ($n=36$) instead (i.e., the victim and attacker were both present). The live experiment confirmed that for Androids graphical patterns video simulation is consistent with the live setting for attacker success rates. However, both 4- and 6-digit PINs demonstrate statistically significant differences in attacker performance, with live attackers performing as much 1.9x better than in the video simulation. The security benefits gained from removing feedback lines in Androids graphical patterns are also greatly diminished in the live setting, particularly under multiple attacker observations, but overall, the data suggests that video recreations can provide a suitable baseline measure for attacker success rate. However, we caution that researchers should consider that these baselines may greatly underestimate the threat of an attacker in live settings.
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