296 research outputs found
A multi-task learning CNN for image steganalysis
Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
the segmentation-based multi-scale analysis to locate tampered areas in digital
images. First, to deal with color input sliding windows of different scales, a
unified CNN architecture is designed. Then, we elaborately design the training
procedures of CNNs on sampled training patches. With a set of robust
multi-scale tampering detectors based on CNNs, complementary tampering
possibility maps can be generated. Last but not least, a segmentation-based
method is proposed to fuse the maps and generate the final decision map. By
exploiting the benefits of both the small-scale and large-scale analyses, the
segmentation-based multi-scale analysis can lead to a performance leap in
forgery localization of CNNs. Numerous experiments are conducted to demonstrate
the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure
Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks
Due to the wide diffusion of JPEG coding standard, the image forensic
community has devoted significant attention to the development of double JPEG
(DJPEG) compression detectors through the years. The ability of detecting
whether an image has been compressed twice provides paramount information
toward image authenticity assessment. Given the trend recently gained by
convolutional neural networks (CNN) in many computer vision tasks, in this
paper we propose to use CNNs for aligned and non-aligned double JPEG
compression detection. In particular, we explore the capability of CNNs to
capture DJPEG artifacts directly from images. Results show that the proposed
CNN-based detectors achieve good performance even with small size images (i.e.,
64x64), outperforming state-of-the-art solutions, especially in the non-aligned
case. Besides, good results are also achieved in the commonly-recognized
challenging case in which the first quality factor is larger than the second
one.Comment: Submitted to Journal of Visual Communication and Image Representation
(first submission: March 20, 2017; second submission: August 2, 2017
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