Artifacts reduction based on separate modes in compressed images

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature.

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Forgery Detection in Digital Images by Multi-Scale Noise Estimation

Journal of Imaging ◽

10.3390/jimaging7070119 ◽

2021 ◽

Vol 7 (7) ◽

pp. 119

Author(s):

Marina Gardella ◽

Pablo Musé ◽

Jean-Michel Morel ◽

Miguel Colom

Keyword(s):

Noise Model ◽

Correlated Noise ◽

Forgery Detection ◽

Color Channel ◽

Image Block ◽

Multi Scale ◽

Compressed Images ◽

Inconsistency Analysis ◽

Highly Correlated ◽

The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

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Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems

Sensors ◽

10.3390/s21010238 ◽

2021 ◽

Vol 21 (1) ◽

pp. 238

Author(s):

Jakub Šalplachta ◽

Tomáš Zikmund ◽

Marek Zemek ◽

Adam Břínek ◽

Yoshihiro Takeda ◽

...

Keyword(s):

High Efficiency ◽

Image Inpainting ◽

Simulated Data ◽

Complementary Metal Oxide Semiconductor ◽

Oxide Semiconductor ◽

Detection Accuracy ◽

Reduction Procedure ◽

X Ray ◽

Artifacts Reduction ◽

Ct Data

In this article, we introduce a new ring artifacts reduction procedure that combines several ideas from existing methods into one complex and robust approach with a goal to overcome their individual weaknesses and limitations. The procedure differentiates two types of ring artifacts according to their cause and character in computed tomography (CT) data. Each type is then addressed separately in the sinogram domain. The novel iterative schemes based on relative total variations (RTV) were integrated to detect the artifacts. The correction process uses the image inpainting, and the intensity deviations smoothing method. The procedure was implemented in scope of lab-based X-ray nano CT with detection systems based on charge-coupled device (CCD) and scientific complementary metal–oxide–semiconductor (sCMOS) technologies. The procedure was then further tested and optimized on the simulated data and the real CT data of selected samples with different compositions. The performance of the procedure was quantitatively evaluated in terms of the artifacts’ detection accuracy, the comparison with existing methods, and the ability to preserve spatial resolution. The results show a high efficiency of ring removal and the preservation of the original sample’s structure.

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