Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

Passive detection of copy-paste forgery between JPEG images

A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.

图像抠图与copy-paste结合的数据增强方法

提出一种基于图像抠图与copy-paste结合的数据增强方法(matting-paste),采用图像抠图法获取单个垃圾实例的准确轮廓,并对单个实例进行旋转和亮度变换.根据物体轮廓信息,把实例粘贴到背景图上,无需额外的人工标注即可生成新的带有标注的数据,从而提高数据集的多样性和复杂性.结果表明:数据集扩充后的mask比数据集扩充前的识别精度提高了0.039,matting-paste能在已有数据集上有效地扩充数据,进一步提高模型的识别精度.

Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.

Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.

An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks tohigh internet speed and high resolution, cheap and easily accessible digitalcameras. We generate, transmit and store millions of images every second.Most of these images are insignificant images containing only personal information.However, in many fields such as banking, finance, public institutions,and educational institutions, the images of many valuable objects like IDcards, photographs, credit cards, and transaction receipts are stored andtransmitted to the digital environment. These images are very significantand must be secured. A valuable image can be maliciously modified by anattacker. The modification of an image is sometimes imperceptible even by theperson who stored the image. In this paper, an active image forgery detectionmethod that encodes and decodes image edge information is proposed. Theproposed method is implemented by designing an interface and applied on atest image which is frequently used in the literature. Various tampering attacksare simulated to test the fidelity of the method. The method not only notifieswhether the image is forged or not but also marks the tampered region ofthe image. Also, the proposed method successfully detected tampered regionsafter geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEGcompression.

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrast-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical techniques.With this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved regions.To perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful vectors.Finally,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

Deep Learning-Based Digital Image Forgery Detection Using Transfer Learning

Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified.In the current cyber world where deepfakes have shaken the global community,confirming the legitimacy of a digital image is of great importance.With the advancements made in deep learning techniques,now we can efficiently train and develop state-of-the-art digital image forensic models.The most traditional and widely used method by researchers is convolution neural networks(CNN)for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.Therefore,in this study,a transfer learning based deep learning technique for image forgery detection is proposed.The proposed methodology consists of three modules namely;preprocessing module,convolutional module,and the classification module.By using our proposed technique,the training time is drastically reduced by utilizing the pre-trained weights.The performance of the proposed technique is evaluated by using benchmark datasets,i.e.,BOW and BOSSBase that detect five forensic types which include JPEG compression,contrast enhancement(CE),median filtering(MF),additive Gaussian noise,and resampling.We evaluated the performance of our proposed technique by conducting various experiments and case scenarios and achieved an accuracy of 99.92%.The results show the superiority of the proposed system.

说话人音频攻击与对抗技术研究综述

文中概括了说话人音频攻击与对抗技术的最新进展。由于说话人音频攻击已经成为语音应用安全的严重威胁,以WaveNet、Transformer和GAN三种模型在音频攻击技术中的应用作为节点,分别介绍以其为基础的音频攻击技术。音频对抗技术则以涵盖的攻击技术分为3类,分别是基础音频攻击、重放攻击和深度伪造攻击。系统地阐述了音频攻击与对抗技术的最新研究成果,并分析比较了各算法在不同条件下的优劣,同时还介绍了音频技术常用的数据集。最后结合该领域目前的研究现状,提出了说话人音频攻防对抗技术研究中亟待关注与研究的问题。

基于多尺度时空特征和篡改概率改善换脸检测的跨库性能

目前大多DeepFake换脸检测算法过于依赖局部特征,尽管库内检测性能尚佳,但容易出现过拟合,导致跨库检测性能不理想,即泛化性能不够好。有鉴于此,文中提出一种基于多尺度时空特征和篡改概率的换脸视频检测算法,目的是利用假脸视频中广泛存在的帧间时域不连续性缺陷来解决现有检测算法在跨库、跨伪造方式和视频压缩时性能明显下降的问题,改善泛化检测能力。该算法包括3个模块:为检测假脸视频在时域上留下的不连续痕迹,设计了一个多尺度时空特征提取模块;为自适应计算多尺度时空特征之间的时空域关联性,设计了一个三维双注意力机制模块;为预测随机选取的像素点的篡改概率和构造监督掩膜,设计了一个辅助监督模块。将所提出的算法在FF++、DFD、DFDC、CDF等公开大型标准数据库中进行实验,并与基线算法和近期发布的同类算法进行对比。结果显示:文中算法在保持库内平均检测性能优良的同时,跨库检测和抗视频压缩时的综合性能最好,跨伪造方法检测时的综合性能中等偏上。实验结果验证了文中算法的有效性。

书商的狡黠:《徐文长佚草》医学文献之谜发覆

自1926年慈溪沈德寿排印本《徐文长佚草》行世后,徐渭有医学著作的信息已然成为事实。然而,在徐渭的诗文中无为人治病及自行处方服药之记录,与其相关人员也未言及其能医。而比沈德寿排印本《徐文长佚草》更早的同名同卷本——清初息耕堂抄本《徐文长佚草》根本没有医学内容。沈德寿排印本《徐文长佚草》中的医学部分其实是明代医官方谷《医林绳墨》卷七、卷八中的内容。

“非羁码”的风险隐忧与前景展望

“非羁码”作为数字技术与检察监督的跨界融合产物,不仅助力实现数字赋能“云”上监管,还顺应了我国数字检察创新发展的要求,有力推动了新型法律监督数字化道路的建设。然而,在适用过程中,“非羁码”存在运行困境以及潜在风险,如:研发机关权力边界不明晰,对犯罪嫌疑人、被告人个人信息保护的法律规定缺位,诱发“数字伪造”“数字接管”,多元化、系统化的监管联合机制尚未建立等。作为一种新型监管手段,“非羁码”应着力降低自身风险隐患,助力维护人权保障与诉讼权利的有机平衡,以实现数字赋能法律监督的新跨越为目标。这具体包括:明确“非羁码”的功能定位与研发机关的权力边界;加强对数字人权的保障;打破思维定式,以“非羁码”使用者的立场为导向;厘清司法主体地位,坚守“技术服务于法治”的初衷;通过优化协作式监管方法,明确各机关之间的责任分工和数字平台建设及应用的职责,构建多元化联合机制,加强对犯罪嫌疑人、被告人的监管等方面的针对性措施,进一步建构和完善应对新时代数字化的监管体系,助力实现“加快建设网络强国、数字强国”的美好愿景。

今本《古文尚书·汤诰》非伪书新证

今本《古文尚书》的《汤诰》从南宋以来被学术界怀疑为所谓“伪古文尚书”之一。本文列举了十八条证据证明今本《汤诰》一定是商代的古本文献,不可能是魏晋人所能伪造的。本文将西汉以前引述《汤诰》的古文献和今本《汤诰》逐一予以精密的比对,论证了只能是春秋战国的文献引述今本《汤诰》,不可能是今本《汤诰》在那些文献的基础上创作而成。尤其是本文发现了今本《汤诰》的“惟皇上帝”一语可与西周金文精准对应,也与清华简精准对应,这是证明今本《汤诰》不伪的坚强证据。今本《汤诰》和《史记》所引《汤诰》是古本《汤诰》的上下篇,都是先秦的真实文献。先秦墨家所传的《尚书》文本和对《尚书》的阐释,与儒家的传本和阐释有所不同。儒家的《尚书》传本和阐释比墨家更可信。

多模态部分伪造数据集的构建与基准检测

针对现有视频伪造数据集缺少多模态伪造场景与部分伪造场景的问题,构建一个综合使用多种音、视频伪造方法的、伪造比例可调的多模态部分伪造数据集PartialFAVCeleb。所提数据集基于FakeAVCeleb多模态伪造数据集,并通过拼接真伪数据构建,其中伪造数据由FaceSwap、FSGAN(Face Swapping Generative Adversarial Network)、Wav2Lip(Wave to Lip)和SV2TTS(Speaker Verification to Text-To-Speech)这4种方法生成。在拼接过程中,使用概率方法生成伪造片段在时域与模态上的定位,并对边界进行随机化处理以贴合实际伪造场景,并通过素材筛选避免背景跳变现象。最终生成的数据集对于每个伪造比例可产生3970条视频数据。在基准检测中,使用多种音视频特征提取器,并分别进行强、弱监督两种条件下的测试,其中弱监督测试基于层次多示例学习(HMIL)方法实现。测试结果显示,各个测试模型在伪造比例较低数据上的性能表现显著低于在伪造比例较高数据上的性能,且弱监督条件下各模型的性能表现显著低于强监督条件下的表现,这验证了该部分伪造数据集的弱监督检测困难性。以上结果表明,以所提数据集为代表的多模态部分伪造场景有充分的研究价值。

基于深度学习的多特征融合人脸鉴伪模型

人脸伪造给网络安全带来了重大挑战。针对现有人脸鉴伪模型特征单一、准确率低的问题,提出了一种基于深度学习的多特征融合人脸鉴伪模型。该模型设计了不同特征提取模块,用以获取不同尺度的特征表示。并学习如何有效融合这些语义信息以准确判定是否伪造,从而显著提升模型的准确率和鲁棒性。最后在公开数据集FaceForensics++上进行大量实验验证。实验结果显示,与现有方法相比,设计的模型有明显的性能提升。

深度视频修复篡改的被动取证研究

深度视频修复技术就是利用深度学习技术,对视频中的缺失区域进行补全或移除特定目标对象。它也可用于合成篡改视频,其篡改后的视频很难通过肉眼辨别真假,尤其是一些恶意修复的视频在社交媒体上传播时,容易造成负面的社会舆论。目前,针对深度视频修复篡改的被动检测技术起步较晚,尽管它已经得到一些关注,但在研究的深度和广度上还远远不够。因此,本文提出一种基于级联Conv GRU和八方向局部注意力的被动取证技术,从时空域角度实现对深度修复篡改区域的定位检测。首先,为了提取修复区域的更多特征,RGB帧和错误级分析帧ELA平行输入编码器中,通过通道特征级融合,生成不同尺度的多模态特征。其次,在解码器部分,使用编码器生成的多尺度特征与串联的Conv GRU进行通道级融合来捕捉视频帧间的时域不连续性。最后,在编码器的最后一级RGB特征后,引入八方向局部注意力模块,该模块通过八个方向来关注像素的邻域信息,捕捉修复区域像素间的异常。实验中,本文使用了VI、OP、DSTT和FGVC四种最新的深度视频修复方法与已有的深度视频修复篡改检测方法HPF和VIDNet进行了对比,性能优于HPF且在编码器参数仅VIDNet的五分之一的情况下获得与VIDNet可比的性能。结果表明,本文所提方法利用多尺度双模态特征和引入的八方向局部注意力模块来关注像素间的相关性,使用Conv GRU捕捉时域异常,实现像素级的篡改区域定位,获得精准的定位效果。

抗高强度椒盐噪声的鲁棒拼接取证算法

在图像取证领域,图像拼接检测技术可以通过分析图像内容识别拼接,并定位拼接区域。然而,在传输、扫描等常见场景中,椒盐(s&p)噪声会不可避免地随机出现,且随着噪声强度的增加,当前拼接取证方法的效力将逐渐减弱,甚至失效,极大地影响了现有拼接取证方法的效果。因此,提出一种能够抵御高强度椒盐噪声的拼接取证算法。所提算法分为2个主要部分:预处理部分和拼接取证部分。首先,预处理部分利用ResNet32与中值滤波器的融合,去除图像中的椒盐噪声,并通过卷积层恢复受损的图像内容,从而最大限度地消除椒盐噪声对拼接取证部分的影响并恢复图像细节;其次,拼接取证部分基于暹罗网络结构,提取与图像唯一性相关的噪声伪影,并通过不一致判断识别拼接区域。在通用篡改数据集上的实验结果表明,所提算法在RGB图像和灰度图像上均取得了良好的效果。在10%噪声场景下与FS(Forensic Similarity)和PSCC-Net(Progressive Spatio-Channel Correlation Network)取证算法相比,所提算法将马修斯相关系数(MCC)值提升超过50%,这验证了所提算法在被噪声干扰的篡改图像上取证的有效性和先进性。

针对低质量视频的双支流人脸伪造检测方法

随着人脸伪造技术不断的发展,如今经过伪造后的视频和图片的人脸伪造质量大幅度提升,这对人身安全、财产安全乃至公共安全存在一定的危害性。因此,迫切需要一种有效的检测方法来区分真假人脸。然而,现有的检测方法面对低质量的虚假人脸视频时存在一定的局限性,即面对压缩过后的低质量视频检测性能较差,此外,泛化性能较差,检测准确率有所下降。为了提升检测网络的准确性和泛化性,将语义信息和噪声信息相结合,提出一个双支流网络,在关注图像语义信息的同时通过高频噪声信息展示出伪造区域和真实区域的不一致性。利用高频噪声信息暴露出的不一致性,重点关注图像语义信息中的伪造痕迹。交互模块增进语义信息和高频信息之间的交互性与融合性。在FaceForensics++数据集进行了训练和测试,并在Celeb-DF数据集上评估该模型的跨数据集泛化性能。从实验结果中可以证明该模型的有效性和可靠性。

基于帧内-帧间自融合的双流泛化人脸伪造检测方法

现有人脸伪造检测方法往往在已知伪造类型上表现良好,但面对未知数据时检测性能有所下降,模型易受到过拟合的影响,检测泛化性不足。针对此问题,提出一种基于帧内-帧间自融合的双流泛化人脸伪造检测方法,从数据增强和检测器改进2个方面提高检测泛化性。设计帧内-帧间自融合模块,分别利用同帧人脸、帧间人脸进行数据增强:帧内自融合子模块利用同帧人脸生成训练数据,从而避免人脸图像身份信息干扰;帧间自融合子模块利用伪造视频的帧间不一致性,进一步构造多样性丰富、逼真的训练数据集,从而有效防止模型的过拟合,确保检测模型的泛化能力。此外,设计基于通道注意力机制的双流特征融合网络,在网络的浅层提取RGB特征、高频特征并进行融合来挖掘伪造信息,在提升模型性能的同时缓解网络的参数增长。将模型在4个数据集上与9种主流检测方法进行对比实验,结果表明:在跨数据集实验中,所提方法较次优方法AUC均值提高1.52个百分点,EER均值降低1.5个百分点;在跨伪造方法实验中,所提方法在4种伪造方法子数据集上均取得最优或次优效果。实验结果验证了该方法优秀的泛化能力。