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  1. Only showing results from aimagelab.ing.unimore.it

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  2. aimagelab.ing.unimore.it

    ELSA Benchmark Media Analytics Overview. The objective of the Multimedia use case is to develop effective solutions for detecting and mitigating the spread of deep fake images in multimedia content. Deep fake images, which are highly realistic and deceptive manipulations, pose significant risks to privacy, security, and trust in digital media ...
  3. aimagelab.ing.unimore.it

    ELSA Deepfake benchmark Keywords: deepfake detection, image generation: CarPatch. ... The Ludovico Antonio Muratori (LAM) dataset is the largest line-level HTR dataset to date and contains 25,823 lines from Italian ancient manuscripts edited by a single author over 60 years.
  4. aimagelab.ing.unimore.it

    ELSA D3 Benchmark - New training dataset released. The current deepfake detection datasets lack diversity in terms of image generators and are insufficient in terms of quantity. To address this limitation, we have developed and released a new dataset named the Diffusion-generated Deepfake Detection (D3) dataset. This dataset comprises almost 2. ...
  5. aimagelab.ing.unimore.it

    ELSA Benchmark Data. D 3 is a multimodal dataset that contains 9.2M generated images, generated with four SoTA diffusion model generators. Each image is generated starting from a LAION-400M caption, ... Each record in the dataset consists of a prompt, a real image, and four images generated with as many generators. ...
  6. aimagelab.ing.unimore.it

    AImageLab is a research laboratory of the Dipartimento di Ingegneria Enzo Ferrari at the University of Modena and Reggio Emilia, Italy. AImageLab is also part of the Artificial Intelligence Research and Innovation Center (AIRI) of the Modena Technopole. a
  7. aimagelab.ing.unimore.it

    Abstract: The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches. In view of this, we aim at extending tracking-by-detection to multi-modal settings, where a comprehensive cost has to be computed from ...
  8. aimagelab.ing.unimore.it

    able some synthetic datasets have also been proposed and largelyadopted(e.g. FVC2000[15]). For emerging activities and open problems instead the main need is to fix some common limits to the discussion and to have an acceptable starting base to compare solu-tions. Often kernel benchmark datasets are defined to stress
  9. aimagelab.ing.unimore.it

    AImageLab is a research laboratory of the Dipartimento di Ingegneria Enzo Ferrari at the University of Modena and Reggio Emilia, Italy. AImageLab is also part of the Artificial Intelligence Research and Innovation Center (AIRI) of the Modena Technopole. a
  10. aimagelab.ing.unimore.it

    In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for many kinds of tests ...
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