Search Results for author:
Found 16 papers, 4 papers with code
Knowledge Graph Driven Recommendation System Algorithm
We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors.
Contrastive Pseudo Learning for Open-World DeepFake Attribution
The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques.
Continual Face Forgery Detection via Historical Distribution Preserving
In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones.
Towards General Visual-Linguistic Face Forgery Detection
To address this issues, in this paper, we propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
End-to-End Reconstruction-Classification Learning for Face Forgery Detection
Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries.
Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning
The progressive enhancement process facilitates the learning of discriminative features with fine-grained face forgery clues.
Dual Contrastive Learning for General Face Forgery Detection
With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns.
Local Relation Learning for Face Forgery Detection
Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern.
DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results
This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection.
Generalized Operating Procedure for Deep Learning: an Unconstrained Optimal Design Perspective
In this paper, we intend to address this problem by presenting a generalized operating procedure for DL from the perspective of unconstrained optimal design, which is motivated by a simple intension to remove the barrier of using DL, especially for those scientists or engineers who are new but eager to use it.
Multi-stream Convolutional Neural Network with Frequency Selection for Robust Speaker Verification
For the diversity of temporal embeddings, we consider feature augmentation with frequency selection, which is to manually segment the full-band of frequency into several sub-bands, and the feature extractor of each stream can select which sub-bands to use as target frequency domain.
ShaneRun System Description to VoxCeleb Speaker Recognition Challenge 2020
In this report, we describe the submission of ShaneRun's team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020.
Hadamard Codebook Based Deep Hashing
Specifically, we utilize an off-the-shelf algorithm to generate a binary Hadamard codebook to satisfy the requirement of bit independence and bit balance, which subsequently serves as the desired outputs of the hash functions learning.
Supervised Online Hashing via Similarity Distribution Learning
In this paper, we propose to model the similarity distributions between the input data and the hashing codes, upon which a novel supervised online hashing method, dubbed as Similarity Distribution based Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship in the produced Hamming space.
Hadamard Matrix Guided Online Hashing
We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code.
