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CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING

Citation Author(s):
Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi
Submitted by:
Ehsan Nowroozi
Last updated:
4 October 2018 - 11:44am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Ehsan Nowroozi
Paper Code:
2247
Categories:

Abstract 

Abstract: 

Detection of contrast adjustments in the presence of JPEG post processing is known to be a challenging task. JPEG post-processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training a JPEG-aware version of the CNN, i.e., feeding the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well under a wide range of QFs and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.

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Dataset Files

ICIP 2018 - poster - MB1.pdf

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