231 research outputs found
Autoencoder with recurrent neural networks for video forgery detection
Video forgery detection is becoming an important issue in recent years,
because modern editing software provide powerful and easy-to-use tools to
manipulate videos. In this paper we propose to perform detection by means of
deep learning, with an architecture based on autoencoders and recurrent neural
networks. A training phase on a few pristine frames allows the autoencoder to
learn an intrinsic model of the source. Then, forged material is singled out as
anomalous, as it does not fit the learned model, and is encoded with a large
reconstruction error. Recursive networks, implemented with the long short-term
memory model, are used to exploit temporal dependencies. Preliminary results on
forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security,
and Forensics, January 201
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
A survey on passive digital video forgery detection techniques
Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of this paper is to provide a comprehensive review of the passive methods for detecting video forgeries. This survey has the primary goal of studying and analyzing the existing passive techniques for detecting video forgeries. First, an overview of the basic information needed to understand video forgery detection is presented. Later, it provides an in-depth understanding of the techniques used in the spatial, temporal, and spatio-temporal domain analysis of videos, datasets used, and their limitations are reviewed. In the following sections, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed in more depth. Finally, identifying loopholes in existing surveys so detecting forged videos much more effectively in the future are discussed
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
Copy-move forgery is a manipulation of copying and pasting specific patches
from and to an image, with potentially illegal or unethical uses. Recent
advances in the forensic methods for copy-move forgery have shown increasing
success in detection accuracy and robustness. However, for images with high
self-similarity or strong signal corruption, the existing algorithms often
exhibit inefficient processes and unreliable results. This is mainly due to the
inherent semantic gap between low-level visual representation and high-level
semantic concept. In this paper, we present a very first study of trying to
mitigate the semantic gap problem in copy-move forgery detection, with spatial
pooling of local moment invariants for midlevel image representation. Our
detection method expands the traditional works on two aspects: 1) we introduce
the bag-of-visual-words model into this field for the first time, may meaning a
new perspective of forensic study; 2) we propose a word-to-phrase feature
description and matching pipeline, covering the spatial structure and visual
saliency information of digital images. Extensive experimental results show the
superior performance of our framework over state-of-the-art algorithms in
overcoming the related problems caused by the semantic gap.Comment: 13 pages, 11 figure
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Deepfake has taken the world by storm, triggering a trust crisis. Current
deepfake detection methods are typically inadequate in generalizability, with a
tendency to overfit to image contents such as the background, which are
frequently occurring but relatively unimportant in the training dataset.
Furthermore, current methods heavily rely on a few dominant forgery regions and
may ignore other equally important regions, leading to inadequate uncovering of
forgery cues. In this paper, we strive to address these shortcomings from three
aspects: (1) We propose an innovative two-stream network that effectively
enlarges the potential regions from which the model extracts forgery evidence.
(2) We devise three functional modules to handle the multi-stream and
multi-scale features in a collaborative learning scheme. (3) Confronted with
the challenge of obtaining forgery annotations, we propose a Semi-supervised
Patch Similarity Learning strategy to estimate patch-level forged location
annotations. Empirically, our method demonstrates significantly improved
robustness and generalizability, outperforming previous methods on six
benchmarks, and improving the frame-level AUC on Deepfake Detection Challenge
preview dataset from 0.797 to 0.835 and video-level AUC on CelebDFv1
dataset from 0.811 to 0.847. Our implementation is available at
https://github.com/sccsok/Locate-and-Verify.Comment: 10 pages, 8 figures, 60 references. This paper has been accepted for
ACM MM 202
Image and Video Forensics
Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity
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