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Found 18 papers, 6 papers with code
Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
Recently, the Image Prompt Adapter (IP-Adapter) has been increasingly integrated into text-to-image diffusion models (T2I-DMs) to improve controllability.
ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction
Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation.
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
To circumvent these issues, in this paper, we propose a novel uncertainty-aware distributional adversarial training method, which enforces adversary modeling by leveraging both the statistical information of adversarial examples and its corresponding uncertainty estimation, with the goal of augmenting the diversity of adversaries.
A Grey-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse
Recent advancements in generative AI, particularly Latent Diffusion Models (LDMs), have revolutionized image synthesis and manipulation.
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs
Dynamic Facial Expression Recognition (DFER) is crucial for understanding human behavior.
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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).
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples
Existing methods typically align probability distributions of natural and adversarial samples between teacher and student models but they overlook intermediate adversarial samples along the "adversarial path" formed by the multi-step gradient ascent of a sample towards the decision boundary.
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners
To bridge this gap we propose a novel framework unifying adversarially robust similarity learning and class concept learning.
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
In addition, to enhance the classification boundaries, we sample and cluster high- and low-confidence features separately based on confidence estimation, facilitating the generation of prototypes closer to the class boundaries.
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.
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges
Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice.
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.
