Inter-frame Accelerate Attack against Video Interpolation Models

• URL: http://arxiv.org/abs/2305.06540v1
• Date: Thu, 11 May 2023 03:08:48 GMT
• Title: Inter-frame Accelerate Attack against Video Interpolation Models
• Authors: Junpei Liao, Zhikai Chen, Liang Yi, Wenyuan Yang, Baoyuan Wu, Xiaochun Cao
• Abstract summary: We apply adversarial attacks to VIF models and find that the VIF models are very vulnerable to adversarial examples. We propose a novel attack method named Inter-frame Accelerate Attack (IAA) thats the iterations as the perturbation for the previous adjacent frame. It is shown that our method can improve attack efficiency greatly while achieving comparable attack performance with traditional methods.
• Score: 73.28751441626754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning based video frame interpolation (VIF) method, aiming to synthesis the intermediate frames to enhance video quality, have been highly developed in the past few years. This paper investigates the adversarial robustness of VIF models. We apply adversarial attacks to VIF models and find that the VIF models are very vulnerable to adversarial examples. To improve attack efficiency, we suggest to make full use of the property of video frame interpolation task. The intuition is that the gap between adjacent frames would be small, leading to the corresponding adversarial perturbations being similar as well. Then we propose a novel attack method named Inter-frame Accelerate Attack (IAA) that initializes the perturbation as the perturbation for the previous adjacent frame and reduces the number of attack iterations. It is shown that our method can improve attack efficiency greatly while achieving comparable attack performance with traditional methods. Besides, we also extend our method to video recognition models which are higher level vision tasks and achieves great attack efficiency.

Related papers

• ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack [71.2286719703198]
  We propose the Recursive Token Merging for Video Diffusion-based Unrestricted Adrial Attack (ReToMe-VA) To achieve spatial imperceptibility, ReToMe-VA adopts a Timestep-wise Adrial Latent Optimization (TALO) strategy. To achieve temporal imperceptibility, ReToMe-VA introduces a Recursive Token Merging (ReToMe) mechanism by matching and merging tokens across video frames.
  arXiv  Detail & Related papers  (2024-08-10T08:10:30Z)
• Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models [47.72177312801278]
  Adrial attacks on Face Recognition (FR) systems have proven highly effective in compromising pure FR models. We propose a novel setting of adversarially attacking both FR and Face Anti-Spoofing (FAS) models simultaneously. We introduce a new attack method, namely Style-aligned Distribution Biasing (SDB), to improve the capacity of black-box attacks on both FR and FAS models.
  arXiv  Detail & Related papers  (2024-05-27T08:30:29Z)
• E-VFIA : Event-Based Video Frame Interpolation with Attention [8.93294761619288]
  We propose an event-based video frame with attention (E-VFIA) as a lightweight kernel-based method. E-VFIA fuses event information with standard video frames by deformable convolutions to generate high quality interpolated frames. The proposed method represents events with high temporal resolution and uses a multi-head self-attention mechanism to better encode event-based information.
  arXiv  Detail & Related papers  (2022-09-19T21:40:32Z)
• CARBEN: Composite Adversarial Robustness Benchmark [70.05004034081377]
  This paper demonstrates how composite adversarial attack (CAA) affects the resulting image. It provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level. A leaderboard to benchmark adversarial robustness against CAA is also introduced.
  arXiv  Detail & Related papers  (2022-07-16T01:08:44Z)
• Cross-Modal Transferable Adversarial Attacks from Images to Videos [82.0745476838865]
  Recent studies have shown that adversarial examples hand-crafted on one white-box model can be used to attack other black-box models. We propose a simple yet effective cross-modal attack method, named as Image To Video (I2V) attack. I2V generates adversarial frames by minimizing the cosine similarity between features of pre-trained image models from adversarial and benign examples.
  arXiv  Detail & Related papers  (2021-12-10T08:19:03Z)
• Sparse Adversarial Video Attacks with Spatial Transformations [26.835631227839837]
  We propose an adversarial attack strategy on videos, called DeepSAVA. Our model includes both additive perturbation and spatial transformation by a unified optimisation framework. DeepSAVA performs a very sparse attack on videos for maintaining human imperceptibility while still achieving state-of-the-art performance in terms of both attack success rate and adversarial transferability.
  arXiv  Detail & Related papers  (2021-11-10T00:37:35Z)
• Boosting the Transferability of Video Adversarial Examples via Temporal Translation [82.0745476838865]
  adversarial examples are transferable, which makes them feasible for black-box attacks in real-world applications. We introduce a temporal translation attack method, which optimize the adversarial perturbations over a set of temporal translated video clips. Experiments on the Kinetics-400 dataset and the UCF-101 dataset demonstrate that our method can significantly boost the transferability of video adversarial examples.
  arXiv  Detail & Related papers  (2021-10-18T07:52:17Z)
• Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior [63.11478060678794]
  We propose an effective motion-excited sampler to obtain motion-aware noise prior. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries.
  arXiv  Detail & Related papers  (2020-03-17T10:54:12Z)
• Sparse Black-box Video Attack with Reinforcement Learning [14.624074868199287]
  We formulate the black-box video attacks into a Reinforcement Learning framework. The environment in RL is set as the recognition model, and the agent in RL plays the role of frame selecting. We conduct a series of experiments with two mainstream video recognition models.
  arXiv  Detail & Related papers  (2020-01-11T14:09:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.