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Found 11 papers, 5 papers with code
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency.
Reinforcement Unlearning
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners.
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
We separate the attack into three stages: style reference selection, reinforcement-learning-based logo style transfer, and perturbation optimization.
Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
Hence, it provides a way to directly generate stego images from secret images without a cover image.
The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
However, recent advanced backdoor attacks show that this assumption is no longer valid in dynamic backdoors where the triggers vary from input to input, thereby defeating the existing defenses.
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
KE-QI: A Knowledge Enhanced Article Quality Identification Dataset
With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media.
StyleFool: Fooling Video Classification Systems via Style Transfer
In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system.
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation.
Cryptography and Security
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
Experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes.
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph.
Cryptography and Security
