Image Deblocking Detection Based on a Convolutional Neural Network

Abstract

With the development of multimedia processing technology, it is becoming much easier to manipulate and tamper with digital video without leaving any visual clues. Because video compression is very common in digital videos, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces. Motion JPEG (MJPEG) is one of the most popular video formats, in which each video frame or interlaced field of a digital video sequence is compressed separately as a JPEG image. By splitting the MJPEG video into JPEG image frames, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces. To the best our knowledge, there is no existing method for the forensics of deblocking. In this paper, we propose a novel method to detect deblocking, which can automatically learn feature representations based on a deep learning framework. We first train a supervised convolutional neural network (CNN) to learn the hierarchical features of deblocking operations with labeled patches from the training datasets. The first convolutional layer of the CNN serves as the preprocessing module to efficiently obtain the tampering artifacts. Then, we extract the features for an image with the CNN on the basis of a patch by applying a patch-sized sliding-window to scan the whole image. The generated image representation is then condensed by a simple feature fusion technique, i.e., regional pooling, to obtain the final discriminative feature. The experimental results on several public datasets demonstrate the superiority of the proposed scheme. INDEX TERMS Multimedia forensics, debocking detection, JPEG block artifacts, convolutional neural network.

An efficient post-processing adaptive filtering technique to rectifying the flickering effects

“…Due to a one-dimensional filtering approach, the in-loop processing method enhances the coding efficiency by reducing blocking artifacts amongst adjoining pixels or frames but is unable to process corner outliers. To alleviate blocking artifacts different post-processing approaches such as frequency domain analysis , Projection Onto Convex Sets (POCS) [9][10][11][12][13], waveletbased techniques [8,[20][21][22][23][24][25][26][27][28][29][30], estimation theory [5,[9][10][11][12][13], and filtering approach [11][12][13][14][15] has been proposed in last few decades. The most common method is to apply a low-pass filter across block boundaries to remove artifacts.…”

“…On the other hand, corner outliers, detection, and removal have been proposed by [15,25]. During compression, the corner outlier pixels are either considerable value or very small value pixels concerning surrounding pixels [8,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Later on, Wang J. et al [37] presented an adaptive filter-based technique for compressed images of different regions.…”

Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach

“…As a matter of fact, a well-trained ML model can be regarded as a Judger who can give more accurate judgement than the crowdsensing system in various applications, e.g., vehicle identification. Correspondingly, a large amount of data is collected that we can use to train a more accurate ML model, which has favorable results for both crowdsensing and ML [20,21]. In particular, ImageNet is an image database that has collected hundreds and thousands of labelled images.…”

“…coins(v) is used to denote an item that the amount is v and coin(v) t 0 denotes the cryptocurrency v to be locked in a smart contract for t 0 times. For simplicity, we utilize a supervised learning [20] to estimate the users' data qualities without knowing a ground truth (our approach can be extended to support other machine learning algorithms), where the quality level is labelled as Q � q 1 , . .…”

AGARNet: Adaptively Gated JPEG Compression Artifacts Removal Network for a Wide Range Quality Factor

“…Hence, we conduct experiments on the synthetic and real-world recompressed images. For experiments on synthetically recompressed images, we divide the Q range [10,80] into four parts as [10,30) them as low, mid, high, ulthigh (ultra-high) image quality respectively. Then, we assume some practical recompression cases as in the first column of Table 3.…”