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Found 11 papers, 1 papers with code
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
Neural style transfer (NST) generates new images by combining the style of one image with the content of another.
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability
Recently, many studies utilized adversarial examples (AEs) to raise the cost of malicious image editing and copyright violation powered by latent diffusion models (LDMs).
Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey
Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists.
XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation
Despite the promising performance of existing visual models on public benchmarks, the critical assessment of their robustness for real-world applications remains an ongoing challenge.
Boundary-Refined Prototype Generation: A General End-to-End Paradigm for Semi-Supervised Semantic Segmentation
Prototype-based classification is a classical method in machine learning, and recently it has achieved remarkable success in semi-supervised semantic segmentation.
Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation
However, a recent work showed the inequality phenomena in $l_{\infty}$-adversarial training and revealed that the $l_{\infty}$-adversarially trained model is vulnerable when a few important pixels are perturbed by i. i. d.
Adversarial Attack and Defense for Medical Image Analysis: Methods and Applications
In this exposition, we present a comprehensive survey on recent advances in adversarial attack and defense for medical image analysis with a novel taxonomy in terms of the application scenario.
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
To circumvent this issue, we propose a novel adversarial training scheme that encourages the model to produce similar outputs for an adversarial example and its ``inverse adversarial'' counterpart.
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping
DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person.
GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021
In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality.
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations
Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.
