Adversarial Bullet-Screen Comment Attack on Video Recognition Models
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Solution Overview
Problem
Existing video recognition models are vulnerable to adversarial attacks, particularly in the physical world, where patch-based attacks are more effective but challenging to implement due to the high dimensionality and correlation of video frames, making it difficult to generate inconspicuous and efficient adversarial patches.
Innovation Solution
A novel adversarial bullet-screen comment (BSC) attack approach using Reinforcement Learning (RL) to optimize the position and transparency of BSCs within video frames, disguising them as meaningful comments to evade detection while maximizing the fooling rate of video recognition models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patch-based attacks are used to generate adversarial patches, then the attack effectiveness is improved, but the difficulty of implementation increases due to high dimensionality and frame correlation
Solution Approach 1:
The video is segmented into individual frames, and the attack is applied selectively to key frames rather than all frames. This reduces the dimensionality of the attack space while maintaining effectiveness by exploiting temporal correlation in video data.
Solution Approach 2:
Instead of applying adversarial patches to all video frames (excessive action), the method applies patches only to selected key frames (partial action). This partial application reduces computational complexity while still achieving effective attacks due to the temporal correlation between frames.
2Reliability
If adversarial patches are applied to each video frame, then the fooling rate is improved, but the computation cost significantly increases
Solution Approach 1:
The video sequence is segmented into key frames that are sufficient to maintain attack effectiveness. By identifying and attacking only these critical frames rather than all frames, the computational cost is significantly reduced while preserving the fooling rate.
Solution Approach 2:
The method uses partial action by applying adversarial patches only to a subset of frames (key frames) rather than all frames. This partial application maintains high fooling rates due to temporal correlation while dramatically reducing computation cost.
3Reliability
If adversarial patches are added to multiple frames, then the attack coverage is improved, but the perceptibility of the attack increases
Solution Approach 1:
Instead of applying patches to multiple frames (which would increase perceptibility), the method applies patches to a single carefully selected key frame. This partial application maintains attack coverage through temporal propagation while minimizing perceptibility.
Solution Approach 2:
The method performs preliminary analysis to identify the most effective single frame for patch placement before applying the attack. This preliminary action ensures that the attack coverage is maximized through temporal correlation while keeping the number of visible patches minimal.
Data Source
AI summary
A method for generating adversarial attacks on a video recognition model is disclosed, including (a) generating the content of bullet-screen comments (BSCs) by an image captioning model for a clean video sample; (b) generating an adversarial video sample by inserting the BSCs into the clean video sample; (c) using the adversarial video sample to attack the video recognition model; (d) receiving rewards from the environment; (e) optimizing the position and transparency of BSCs by an reinforced learning (RL) agent according to the received rewards; (f) updating the adversarial video sample and using it to attack the video recognition model; and iteratively repeating steps of (d)-(f), until a predefined condition is matched.