45 citations · 113 across the 33 of their papers we have counts for
5 papers · 2 filters
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging
Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1
We propose a novel bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropaga…
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection
Davide Alessandro Coccomini, Roberto Caldelli, Fabrizio Falchi +2
Image manipulation is rapidly evolving, allowing the creation of credible content that can be used to bend reality. Although the results of deepfake detectors are promising, deepfa…
Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting
Luca Ciampi, Nicola Messina, Matteo Pierucci +3
Recently, object counting has shifted towards class-agnostic counting (CAC), which counts instances of arbitrary object classes never seen during model training. With advancements…
Adversarial Magnification to Deceive Deepfake Detection through Super Resolution
Davide Alessandro Coccomini, Roberto Caldelli, Giuseppe Amato +2
Deepfake technology is rapidly advancing, posing significant challenges to the detection of manipulated media content. Parallel to that, some adversarial attack techniques have bee…
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
Davide Alessandro Coccomini, Roberto Caldelli, Claudio Gennaro +3
Deepfake detectors are typically trained on large sets of pristine and generated images, resulting in limited generalization capacity; they excel at identifying deepfakes created t…