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Found 27 papers, 8 papers with code
Towards General Visual-Linguistic Face Forgery Detection(V2)
Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust.
Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing
To intervene these biases, we propose two branches for shuffling and mixing with tokens in the latent space of transformers.
Graph Canvas for Controllable 3D Scene Generation
Spatial intelligence is foundational to AI systems that interact with the physical world, particularly in 3D scene generation and spatial comprehension.
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
AI-generated images (AIGIs), such as natural or face images, have become increasingly realistic and indistinguishable, making their detection a critical and pressing challenge.
A Quality-Centric Framework for Generic Deepfake Detection
To tackle this issue, we propose a novel quality-centric framework for generic deepfake detection, which is composed of a Quality Evaluator, a low-quality data enhancement module, and a learning pacing strategy that explicitly incorporates forgery quality into the training process.
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion
The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content.
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction
3D Gaussian Splatting (3DGS) has emerged as a promising approach for 3D scene representation, offering a reduction in computational overhead compared to Neural Radiance Fields (NeRF).
Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
To reproduce FFD, we then propose a novel Video-level Blending data (VB), where VB is implemented by blending the original image and its warped version frame-by-frame, serving as a hard negative sample to mine more general artifacts.
ScalingGaussian: Enhancing 3D Content Creation with Generative Gaussian Splatting
We introduce a novel 3D content creation framework, ScalingGaussian, which combines 3D and 2D diffusion models to achieve detailed textures and geometric consistency in generated 3D assets.
DF40: Toward Next-Generation Deepfake Detection
In this work, we found the dataset (both train and test) can be the "primary culprit" due to: (1) forgery diversity: Deepfake techniques are commonly referred to as both face forgery and entire image synthesis.
Rank-based No-reference Quality Assessment for Face Swapping
Extensive experiments confirm the superiority of our method over existing general no-reference image quality assessment metrics and the latest metric of facial image quality assessment, making it well suited for evaluating face swapping images in real-world scenarios.
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.
