← Back to all news
arXiv Machine Learning

Camera-Noise Residuals for Face-Swap Detection: Redundant, Not Complementary, and Why

The study evaluates whether adding a camera‑noise fingerprint (Noiseprint++) to an RGB Xception backbone improves face‑swap detection on FaceForensics++. A three‑model ablation shows that fusion does not outperform RGB alone and that the residual branch alone performs near chance. Diagnostic analysis reveals that the noise maps contain a statistical signal in their per‑sample moments, but an InstanceNorm layer standardizes these away, rendering the residual redundant rather than complementary to RGB features.

Related stories

arXiv Computer Vision
3d ago

RLDF: A Region-Level Deepfake Dataset and Benchmark for Partial-Face Manipulation Detection

RLDF (Region-Level Deepfake) is the first dataset designed to evaluate deepfake detection on partial facial manipulations. It contains over 2 million partial face‑swap images generated with eight face‑swapping methods across seven facial regions, including both discrete (eye, nose, mouth, eyebrow) and composite regions. The authors benchmark 12 state‑of‑the‑art detectors on five cross‑dataset protocols, revealing a 10.5 % drop in AUC and 10 % drop in accuracy for region‑level detection, and propose a mask‑guided cross‑attention model as a baseline.

By Shukesh Reddy, Abhijit Das
roboticsbenchmarks
More like this →
arXiv Machine Learning
Oct 9

Does an Illumination Prior Help Face-Swap Detection? A Controlled Study of Temporal Self-Blended Images

The study examines whether adding illumination inconsistencies to self-blended images—creating Temporal Self-Blended Images (T‑SBI)—enhances face‑swap detection. Across five training regimes and multiple datasets, no significant improvement in detection accuracy was observed; AUC differences stayed within seed variability. However, T‑SBI altered prediction score distributions, shifting optimal thresholds, and did improve robustness to heavy JPEG compression on the DFDC dataset, suggesting a reliance on low‑frequency cues.

By Danil Davydov, Bader Rasheed, Dmitriy Vatolin
More like this →
arXiv Computer Vision
Sep 14

RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation

RA‑SOD is a new RGB‑Thermal salient object detection framework that explicitly models the reliability of each modality. It introduces a reliability‑conditioned representation, an uncertainty‑guided dual‑stream refinement, and a pixel‑wise modality competition mechanism to adaptively compensate degraded features and suppress unreliable evidence. Experiments on four benchmarks show that RA‑SOD achieves state‑of‑the‑art performance and remains robust under severe modality degradation.

By Hongbo Gao, Zhengyu Li, Xueru Nie, Dihao Zhu, Lijun Zhao, Yunke Wang, Chang Xu
computer-visionmultimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 28

The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation

The paper investigates how knowledge distillation from event cameras to RGB images can alter the inductive biases of convolutional neural networks. By transferring learning from the event domain, the authors find that models gain color invariance, a shape bias, and improved robustness to high‑frequency noise, largely due to reduced reliance on texture and increased emphasis on edge‑based object shape. These changes are evidenced by early‑layer processing differences and a spectral trade‑off between robustness to missing high‑frequency content and vulnerability to its contamination or geometric disruption.

By Soshun Kihara, Shunsuke Yasuki, Masato Taki
efficiencybenchmarkssafety
More like this →
arXiv Machine Learning
Sep 7

Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments

The paper introduces AdaGate-DF, an adaptive gated deepfake detection framework designed for low-resolution and resource-constrained environments. By leveraging image-quality cues, the system routes samples through a dual multi-exit architecture, allowing high-quality images to exit earlier and reduce computational load. Evaluations on Celeb-DF and FaceForensics++ show that AdaGate-DF outperforms existing models such as MaD-CoRN and DefakeHop++ while maintaining low inference latency and robust performance across varying resolutions and class imbalance scenarios.

By Vaishnavi Sen, Cody Laurie, Rashida Hasan
efficiencybenchmarks
More like this →