Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Audio Splicing Detection and Localization Based on Acquisition Device Traces

In recent years, the multimedia forensic community has put a great effort in developing solutions to assess the integrity and authenticity of multimedia objects, focusing especially on manipulations applied by means of advanced deep learning techniques. However, in addition to complex forgeries as the deepfakes, very simple yet effective manipulation techniques not involving any use of state-of-the-art editing tools still exist and prove dangerous. This is the case of audio splicing for speech signals, i.e., to concatenate and combine multiple speech segments obtained from different recordings of a person in order to cast a new fake speech. Indeed, by simply adding a few words to an existing speech we can completely alter its meaning. In this work, we address the overlooked problem of detection and localization of audio splicing from different models of acquisition devices. Our goal is to determine whether an audio track under analysis is pristine, or it has been manipulated by splicing one or multiple segments obtained from different device models. Moreover, if a recording is detected as spliced, we identify where the modification has been introduced in the temporal dimension. The proposed method is based on a Convolutional Neural Network (CNN) that extracts model-specific features from the audio recording. After extracting the features, we determine whether there has been a manipulation through a clustering algorithm. Finally, we identify the point where the modification has been introduced through a distance-measuring technique. The proposed method allows to detect and localize multiple splicing points within a recording

Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art

Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos

Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena

Towards Tamper-Evident Storage on Patterned Media

We propose a tamper-evident storage system based on probe storage with a patterned magnetic medium. This medium supports normal read/write operations by out-of-plane magnetisation of individual magnetic dots. We report on measurements showing that in principle the medium also supports a separate class of write-once operation that destroys the out-of-plane magnetisation property of the dots irreversibly by precise local heating. We discuss the main issues of designing a tamper-evident storage device and file system using the properties of the medium

Improved Tampering Localization in Digital image Forensics: Comparative Study Based on Maximal Entropy Random Walk and Multi-Scale Fusion

Nowadays the increasing ease of editing digital photographs has spawned an urgent need for reliable authentication mechanism capable of precise localization of potential malicious forgeries. In this paper we compare two different Techniques to analyze which technique can be used more efficiently in localization of Tampered Region In Digital Image .First Technique is Maximal Entropy Random Walk in which Strong localization property of this random walk will highlight important regions and to diminish the background- even for noisy response maps. Our evaluation will show that the proposed method can significantly perform both the commonly used threshold-based decision, and the recently proposed optimization approach with a Markovian prior. The second Technique which is based on Multi-Scale Fusion will investigate a multi-scale analysis approach which merge multiple candidate tampering maps, obtained from the analysis with different windows, to obtain a single, more efficient tampering map with better localization resolution. We propose three different techniques for multi- scale fusion, and verify their feasibility .In this slant we consider popular tampering scenario to distinguish between singly and doubly compressed region

Anti- Forensics: The Tampering of Media

In the context of forensic investigations, the traditional understanding of evidence is changing where nowadays most prosecutors, lawyers and judges heavily rely on multimedia signs. This modern shift has allowed the law enforcement to better reconstruct the crime scenes or reveal the truth of any critical event.In this paper we shed the light on the role of video, audio and photos as forensic evidences presenting the possibility of their tampering by various easy-to-use, available anti-forensics softwares. We proved that along with the forensic analysis, digital processing, enhancement and authentication via forgery detection algorithms to testify the integrity of the content and the respective source of each, differentiating between an original and altered evidence is now feasible. These operations assist the court to attain higher degree of intelligibility of the multimedia data handled and assert the information retrieved from each that support the success of the investigation process

Frame-synchronous Blind Audio Watermarking for Tamper Proofing and Self-Recovery

This paper presents a lifting wavelet transform (LWT)-based blind audio watermarking scheme designed for tampering detection and self-recovery. Following 3-level LWT decomposition of a host audio, the coefficients in selected subbands are first partitioned into frames for watermarking. To suit different purposes of the watermarking applications, binary information is packed into two groups: frame-related data are embedded in the approximation subband using rational dither modulation; the source-channel coded bit sequence of the host audio is hidden inside the 2nd and 3rd -detail subbands using 2N-ary adaptive quantization index modulation. The frame-related data consists of a synchronization code used for frame alignment and a composite message gathered from four adjacent frames for content authentication. To endow the proposed watermarking scheme with a self-recovering capability, we resort to hashing comparison to identify tampered frames and adopt a Reed–Solomon code to correct symbol errors. The experiment results indicate that the proposed watermarking scheme can accurately locate and recover the tampered regions of the audio signal. The incorporation of the frame synchronization mechanism enables the proposed scheme to resist against cropping and replacement attacks, all of which were unsolvable by previous watermarking schemes. Furthermore, as revealed by the perceptual evaluation of audio quality measures, the quality degradation caused by watermark embedding is merely minor. With all the aforementioned merits, the proposed scheme can find various applications for ownership protection and content authentication