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A vision-based fully automated approach to robust image cropping detection

  • Published:2020-02 Issue: Volume:80 Page:115629
  • ISSN:0923-5965
  • Container-title:Signal Processing: Image Communication
  • language:en
  • Short-container-title:Signal Processing: Image Communication

Author:

Fanfani Marco,Iuliani Massimo,Bellavia Fabio,Colombo Carlo,Piva Alessandro

Funder

Air Force Research Laboratory

Defense Advanced Research Projects Agency

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. A survey of image forgery detection;Farid;IEEE Signal Process. Mag.,2009

2. Information forensics: An overview of the first decade;Stamm;IEEE Access,2013

3. Digital image forgery detection using passive techniques: A survey;Birajdar;Digit. Investig.,2013

4. An overview on image forensics;Piva;ISRN Signal Process.,2013

5. A bibliography of pixel-based blind image forgery detection techniques;Qureshi;Signal Process., Image Commun.,2015

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1. A semi-supervised deep learning approach for cropped image detection;Expert Systems with Applications;2024-06

2. A knowledge distillation based deep learning framework for cropped images detection in spatial domain;Signal Processing: Image Communication;2024-05

3. PulseID: Multi-scale photoplethysmographic identification using a deep convolutional neural network;Biomedical Signal Processing and Control;2024-02

4. Image Protection for Robust Cropping Localization and Recovery;2023 IEEE International Conference on Multimedia and Expo (ICME);2023-07

5. Learning discriminative visual semantic embedding for zero-shot recognition;Signal Processing: Image Communication;2023-07
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