Adversarial Deepfake Generation and an Investigation of Purification-Based Adversarial Detection

arXiv:2607.25842 · cs.CV, cs.CR · Submitted 2026-07-28 · Read on arXiv

Junghyun Kim, Seunghyun Kim, Jiyoung Woo

cs.CV, cs.CR

Submitted: 2026-07-28

Comments: Accepted at CLEF 2026, ImageCLEF-Deepfake task. Published in CEUR-WS CLEF 2026 Working Notes

Code: https://github.com/deepinsight/insightface

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUROC — 44× in this paper · explained in 2 episodes
• Fine-Tuning — 25× in this paper · explained in 5 episodes
• Calibration — 19× in this paper · explained in 7 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Benchmark — 17× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Transferability — 7× in this paper · explained in 4 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Adversarial Attacks — 5× in this paper · explained in 1 episode
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 5× in this paper
• Quantization — 5× in this paper · explained in 12 episodes
• Adversarial Examples — 4× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• International Conference on Computer Vision — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• International Conference on Machine Learning — 3× in this paper
• Linear probe — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Adaptive Weighting — 2× in this paper · explained in 1 episode
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Momentum Iterative — 2× in this paper
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Projected Gradient Descent — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
• PuLID: Pure and Lightning ID Customization via Contrastive Alignment
• DINOv2: Learning Robust Visual Features without Supervision
• DINOv3
• Rethinking Cross-Generator Image Forgery Detection through DINOv3
• The DeepFake Detection Challenge (DFDC) Dataset

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models