Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects

In this paper, a new algorithm is proposed for forgery detection in MPEG videos using spatial and time domain analysis of quantization effect on DCT coefficients of I and residual errors of P frames. The proposed algorithm consists of three modules, including double compression detection, malicious tampering detection and decision fusion. Double compression detection module employs spatial domain analysis using first significant digit distribution of DCT coefficients in I frames to detect single and double compressed videos using an SVM classifier. Double compression does not necessarily imply the existence of malignant tampering in the video. Therefore, malicious tampering detection module utilizes time domain analysis of quantization effect on residual errors of P frames to identify malicious inter-frame forgery comprising frame insertion or deletion. Finally, decision fusion module is used to classify input videos into three categories, including single compressed videos, double compressed videos without malicious tampering and double compressed videos with malicious tampering. The experimental results and the comparison of the results of the proposed method with those of other methods show the efficiency of the proposed algorithm.     

基于时空域相关性的屏幕内容帧间快速编码算法

针对屏幕内容视频帧间编码的高复杂度问题,提出了一种基于时空域特性的帧间快速编码算法。首先,根据运动静止检测算法将待编码帧分为静止帧和运动帧;然后,对运动和静止帧分别采用不同的编码策略。对于静止帧,在统计分析时域对应编码单元（CU）分割深度和预测模式的基础上,确定CU最佳分割深度和最优预测模式。对于运动帧中的静止最大编码单元（LCU）,利用时域相关特性提前终止CU分割,模式选取则只针对大尺寸模式进行预测;对于运动帧中的运动LCU,根据其相邻LCU的运动静止特性确定CU分割深度以及预测模式。实验结果表明,所提算法相比原始编码平台,在BDBR平均上升3.65%的情况下,编码时间平均节省46.40%。所提算法在率失真性能损失可接受的前提下,有效地降低了屏幕内容视频帧间编码复杂度,有利于屏幕内容视频的实时应用。     

基于帧间相关性的语音活动检测方法

为了提高统计模型似然比测试的语音活动检测(VAD)的检测性能,利用前后语音帧间存在的统计相关特性,提出一种改进VAD算法。通过前帧语音频谱分量对先验信噪比进行递归估计,然后利用前一帧的语音检测状态来设计判决阈值,建立了双阈值隐马尔可夫模型语音活动判决规则。实验表明,此帧间相关性VAD算法的检测指标值优于Sohn算法。     

基于帧间一致性的自监督室内逆渲染

针对目前逆渲染监督学习方法难以获得标签、泛化能力差的问题,提出了一种基于IFC（inter-frame coherence）的自监督训练方法。由于逆渲染问题的不适定性,引入额外的反照率一致性损失和交叉渲染损失强化自监督网络,其主要思想是对连续光照变化的图像序列执行IFC约束。即通过图像帧之间的位姿图和深度图,在相邻帧之间执行图像投影和扭曲;通过这种方法在相邻帧之间建立约束,并使用孪生训练来确保对光度不变量的一致估计。该方法使用完全卷积神经网络从室内视频序列中恢复几何形状、反射率和光照。自监督网络使用没有标签的连续帧图像集合进行训练,通过结合可微分渲染器,使网络以自监督的方式进行学习。通过与其他主流方法的比较,定量和定性实验结果表明提出方法在多个基准上表现更优。     

Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity

Video frame-rate up-conversion is one of the common operations for tampering digital videos in the temporal domain, such as creating fake high-quality videos and splicing two video clips with different frame rates. However, few existing works have been proposed for detecting this form of tampering operation. Based on the analysis of extensive experiments, we found that frame-rate up-conversion algorithms employed in most current video editing softwares will inevitably introduce some periodic artifacts into inter-frame similarity in the resulting video frame sequence. By analyzing such artifacts, we propose a simple yet very effective method to expose video after frame-rate up-conversion, and further estimate its original frame rate. The experimental results evaluated on 100 original videos at different frame rates have shown the effectiveness of the proposed method. The average detection accuracy can achieve as high as 99 % on noise-free videos in uncompressed and H.264/AVC formats. Besides, the proposed method is robust to noise as the detection accuracy could reach over 85 % and 95 % on noised videos with Gaussian white noise when SNR is 33 db and 36 db respectively.     

Video flickering removal using temporal reconstruction optimization

In this paper, we introduce an approach to remove the flickers in the videos, and the flickers are caused by applying image-based processing methods to original videos frame by frame. First, we propose a multi-frame based video flicker removal method. We utilize multiple temporally corresponding frames to reconstruct the flickering frame. Compared with traditional methods, which reconstruct the flickering frame just from an adjacent frame, reconstruction with multiple temporally corresponding frames reduces the warp inaccuracy. Then, we optimize our video flickering method from following aspects. On the one hand, we detect the flickering frames in the video sequence with temporal consistency metrics, and just reconstructing the flickering frames can accelerate the algorithm greatly. On the other hand, we just choose the previous temporally corresponding frames to reconstruct the output frames. We also accelerate our video flicker removal with GPU. Qualitative experimental results demonstrate the efficiency of our proposed video flicker method. With algorithmic optimization and GPU acceleration, the time complexity of our method also outperforms traditional video temporal coherence methods.

     

A novel H.264 rate control algorithm with consideration of visual attention

In video coding, a well-designed rate control scheme should be concerned with both the objective and subjective quality. However, the existing H.264 rate control algorithms mainly aim at improving the objective quality without considering the human visual system. In this paper, we propose a novel rate control algorithm that takes into account visual attention. In a group of pictures, bits allocated to each frame are related to the local motion attention in it, and more bits are allocated to the frames with strong local motion attention. Similarly, in each frame, more bits are assigned to visually significant macroblocks (MBs), and fewer to visually insignificant MBs. Experiment results show that the proposed algorithm improves the coding quality in frames with strong local motion, and reduces PSNR fluctuation across frames by up to 22.15%. In addition, PSNR in visually important regions is increased by up to 1.45 dB as compared to the standard H.264 rate control scheme that improves the subjective quality. Increased computation complexity of the proposed algorithm is less than 4%, which is negligible.     

Video super-resolution reconstruction method based on deep Back projection and motion feature fusion

How to effectively utilize inter-frame redundancies is the key to improve the accuracy and speed of video super-resolution reconstruction methods. Previous methods usually process every frame in the whole video in the same way, and do not make full use of redundant information between frames, resulting in low accuracy or long reconstruction time. In this paper, we propose the idea of reconstructing key frames and non-key frames respectively, and give a video super-resolution reconstruction method based on deep back projection and motion feature fusion. Key-frame reconstruction subnet can obtain key frame features and reconstruction results with high accuracy. For non-key frames, key frame features can be reused by fusing them and motion features, so as to obtain accurate non-key frame features and reconstruction results quickly. Experiments on several public datasets show that the proposed method performs better than the state-of-the-art methods, and has good robustness.

     

一种改进的WLAN中基于MPDU最小帧间间距的帧聚合机制

为降低帧聚合机制开销,提高无线局域网的网络吞吐率,本文设计了一种改进的基于MPDU最小帧间间距的帧聚合机制。首先分析了帧聚合机制开销受数据帧长度的影响,根据最小MPDU帧间间距设定聚合门限来选择聚合方式,并进一步提出了一种自适应A-MSDU聚合算法。最后通过仿真分析验证了所提出的改进帧聚合机制可以保证较高且平稳的吞吐率性能。     

融合多特征的视频帧间篡改检测算法

传统的视频帧间被动取证往往依赖单一特征,而这些特征各自适用于某类视频,对其他视频的检测精度较低。针对这种情况,提出一种融合多特征的视频帧间篡改检测算法。该算法首先计算视频的空间信息和时间信息值并对视频进行分组,接着计算视频帧间连续性VQA特征,然后结合SVM–RFE特征递归消除算法对不同特征排序,最后利用顺序前向选择算法和Adaboost二元分类器对排序好的特征进行筛选与融合。实验结果表明,该算法提高了篡改检测精度。     

Fast object tracking using adaptive block matching

We propose a fast object tracking algorithm that predicts the object contour using motion vector information. The segmentation step common in region-based tracking methods is avoided, except for the initialization of the object. Tracking is achieved by predicting the object boundary using block motion vectors followed by updating the contour using occlusions/disocclusion detection. An adaptive block-based approach has been used for estimating motion between frames. An efficient modulation scheme is used to control the gap between frames used for motion estimation. The algorithm for detecting disocclusion proceeds in two steps. First, uncovered regions are estimated from the displaced frame difference. These uncovered regions are classified into actual disocclusions and false alarms by observing the motion characteristics of uncovered regions. Occlusion and disocclusion are considered as dual events and this relationship is explained in detail. The algorithm for detecting occlusion is developed by modifying the disocclusion detection algorithm in accordance with the duality principle. The overall tracking algorithm is computationally superior to existing region-based methods for object tracking. The immediate applications of the proposed tracking algorithm are video compression using MPEG-4 and content retrieval based on standards like H.264. Preliminary simulation results demonstrate the performance of the proposed algorithm.     

A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exponential transform domain

In this paper, a robust video zero-watermarking scheme for copyright protection using a combination of convolutional neural network (CNN) and self-organizing map (SOM) in polar complex exponential transform (PCET) space is presented. The scheme is developed not only to remedy the existing problems of lacking in some performance assessments but also to enhance the robustness. It starts with extracting the content feature of each frame by CNN and then some significant frames are selected using SOM clustering and maximum entropy. Secondly, the PCET is applied to all selected frames to abstract invariant moments, and further, is scrambled by a chaotic logistic map and is reduced in dimensions by singular value decomposition (SVD). Next, a binary sequence is generated by comparing adjacent values of the obtained compact PCET moments in the previous step, and further is permuted to produce a binary matrix. Finally, a bitwise exclusive-OR operation is imposed on the binary matrix and the encrypted watermark by the chaotic map to generate a zero-watermark signal. Experimental results demonstrate that the proposed scheme has adequate equalization and distinguishability of zero-watermarks as well as strong robustness against common signal processing, geometric, compression, and inter-frame attacks. Also, compared with existing video zero-watermarking and traditional video watermarking methods, the proposed scheme exhibits superior robustness.

     

基于帧间小波变换的视频水印研究

针对数字视频版权保护及信息隐藏技术的需要,本文提出了一种基于帧间小波变换的视频水印算法。该算法首先对视频序列进行等长分组,然后对各组中的视频序列进行最大级数的帧间一维小波变换,从而可以得到一帧低频图像和多帧高频图像,其中低频图像聚集了视频序列的大部分能量,而高频图像的能量相对较小。本文对低频图像进行奇异值分解,然后将水印图像嵌入到低频图像的奇异值变换域上。在水印的嵌入与提取过程中只对4帧视频图像进行小波变换,所需内存较小,并易于硬件实现。实验结果表明,本文提出的视频水印算法具有较好的隐蔽性和鲁棒性。     

一种利用宏块直方图的快速帧间预测方法*

针对H.264/AVC编码复杂度高难以实时应用的缺陷,在深入研究H.264/AVC中重要且耗时的帧间预测编码技术的基础上,提出了一种融合宏块直方图分布信息的分层逐级预判的快速帧间预测算法。仿真实验结果表明,提出算法与H.264/AVC标准算法相比,在保持重建视频图像质量和输出码流结构的前提下,平均节省编码时间约70%,改善了编码复杂度与编码实时性,保持了H.264/AVC低码率的编码优势。该算法有效地提高了H.264/AVC的编码效率。     

Intra/Inter Macroblock Mode Decision for Error-Resilient Transcoding

When transmitting the precoded bitstream over an error-prone network, error-resilient transcoding is adopted to convert the bitstream to a resilient format for robust delivery. Intra refreshment is an efficient tool to reduce the dependency between frames and stop the channel error propagation. In the conventional scheme, the rate-distortion optimized macroblock mode decision is employed to adaptively determine the coding mode of each macroblock. However, this scheme only considers the channel error propagated from the previous frames to the current frame. As opposed to this traditional algorithm, this paper proposes a method which considers consecutive two frames in a sequence, thus taking the error propagation to the following frame into account. This enhances the overall robustness of the transcoded bitstream against the packet loss. Considering the availability of the next frame information, two cases are discussed respectively. Experimental results show that the proposed methods present quality improvement when compared with the conventional rate-distortion optimized error-resilient coding scheme under different test environments, and the PSNR improvement can reach as high as 0.9 dB.     

基于自适应块组割先验的噪声图像超分辨率重建

要增强噪声图像的分辨率,传统的串联方式依次进行去噪与超分辨率重建两个步骤,但去噪算法去除噪声的同时也损失了部分细节信息,影响了后续超分辨率重建的质量.为了使低分辨率噪声图像中所有细节信息都能参与超分辨率重建,本文以非局部中心化稀疏表示（Nonlocally centralized sparse representation,NCSR）模型为基础,提出了基于自适应块组割（Patch-group-cuts,PGCuts）先验的噪声图像超分辨率重建方法,同时实现去噪和超分辨率重建功能.块组割先验基于新颖的三维邻域系统和块组模型,能够达到图像去噪、边缘平滑和边缘清晰等效果.重建时以边缘强度为参考对块组割先验进行自适应约束,由于块组割在平滑区域约束力较低,采用分区域融合的方式进一步抑制噪声.本文对合成的低分辨率噪声图像和真实的低分辨率噪声图像进行了重建实验,实验表明,基于自适应块组割先验的噪声图像超分辨率重建算法,在丰富细节的同时能抑制噪声的干扰,不但具有较高的峰值信噪比和结构相似度等客观评价值,而且在非光滑区域具有很好的主观重建效果.     

利用运动矢量进行视频篡改检测

随着高质量的数字摄像机和功能复杂的视频编辑软件的出现,对视频进行恶意篡改已变得越来越容易;同时迅速庞大的视频数据也使得利用计算机自动检测恶意篡改的需求日益迫切.针对视频恶意篡改的特点并利用帧间内容的连续性,提出通过统计视频帧间预测的运动矢量,检测视频是否被恶意篡改.首先,提取各P帧的运动矢量幅度;然后利用滑动窗口、计算窗口中各运动矢量幅度与窗口中的幅度均值的距离.由于篡改点处运动矢量的幅度与所属窗口均值的偏移较大,从而能够有效检测出篡改及其位置.同时通过改变图像组大小进行再压缩,可以进一步降低漏检概率.实验表明所提方法能够有效地检测出视频在时间轴上的篡改.     

基于H.264的新型I帧码率控制算法

针对H.264视频编码标准,在综合考虑码率、缓冲区状态和视频序列复杂度的基础上,提出一种新型的I帧码率控制算法。使用边缘信息,建立幂函数形式和指数函数形式的2种R-Q模型。基于前4个待编码帧的预编码,估计首个I帧的时间复杂度,第1个I帧的比特分配得到修正。仿真结果证明,该码率控制算法可以有效提高重建视频的峰值信噪比(PSNR),降低PSNR波动,并大幅减少跳帧情况的发生。     

一种自适应选取参考帧的多视点视频编码方案

多视点视频是目前视频编码的研究热点之一。为了提高多视点视频的压缩率和视觉质量,本文提出了一种自适应地选取B帧作为参考帧的编码方案。通过将相邻的两个P帧或I帧间的残差与两个预先定义的阈值进行比较,该方案自适应地选取B帧作为参考帧,从而获得较好的压缩效率和视觉效果。该方案采用H.264/AVC的编码器JM实现,并取得了预期的测试结果。     

视频对象自动分割算法

提出了一种基于小波变换和运动边缘检测的自动视频对象分割方法。该方法对相邻帧分别进行二维小波变换，在小波域进行变化检测和Canny边缘提取，返回空间域后得到鲁棒的相邻帧差分边缘模板。结合当前帧边缘、背景边缘和前一帧的运动边缘，检测出当前帧对象的运动边缘，从而实现对视频对象的提取。通过实验验证，该算法对目标的整体运动和局部形变都有较强的适应性。此外，算法还具有快速、简便、准确性高的特点。