mlsanityData to 7 Oct 2026

Adversarial Framing for Image and Video Classification

arXiv 1812.04599 ↗First posted 11 Dec 2018cs.CV cs.AI cs.LGcited by 15

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Abstract (arXiv)

Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it with a patch. In this paper, we propose a method that keeps the image unchanged and only adds an adversarial framing on the border of the image. We show empirically that our method is able to successfully attack state-of-the-art methods on both image and video classification problems. Notably, the proposed method results in a universal attack which is very fast at test time. Source code can be found at https://github.com/zajaczajac/adv_framing .

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2018.

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Citing papers per year: 2021: 7; 2022: 2; 2023: 3; 2024: 2; 2025: 1.

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  1. 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fastcited by 149
  2. 2024Test-Time Backdoor Attacks on Multimodal Large Language Modelscited by 35
  3. 2021Adversarially Robust One-class Novelty Detectioncited by 24
  4. 2021Adversarial Robustness against Multiple and Single lp-Threat Models via Quick Fine-Tuning of Robust Classifierscited by 23
  5. 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
  6. 2022StyleFool: Fooling Video Classification Systems via Style Transfercited by 18
  7. 2021Parametric Scattering Networkscited by 15
  8. 2021Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Modelscited by 12
  9. 2025Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videoscited by 9
  10. 2023Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Predictioncited by 8
  11. 2021Thinking Beyond Distributions in Testing Machine Learned Modelscited by 5
  12. 2022On the interplay of adversarial robustness and architecture components: patches, convolution and attentioncited by 4
3 more
  1. 2021Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentationcited by 3
  2. 2023Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundarycited by 1
  3. 2023Optimizing the AI Development Process by Providing the Best Support Environmentcited by 0