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Found 6 papers, 3 papers with code
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models
The detection of malicious deepfakes is a constantly evolving problem that requires continuous monitoring of detectors to ensure they can detect image manipulations generated by the latest emerging models.
TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact.
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
Face Morphing Attack Detection Using Privacy-Aware Training Data
Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image.
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection
Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficient anomaly detection across a diverse set of anomaly detection tasks.
