Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact, image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. However, some specific image contents are hardly retained by CNN-based detection networks, but if included, would improve the detection accuracy of the networks. To resolve these issues, we propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Unet for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection, without requiring pre-training or training on a large number of forgery images. Moreover, it was stably robust to different attacks.
In the world of fake news and deepfakes, there have been an alarmingly large number of cases of images being tampered with and published in newspapers, used in court, and posted on social media for defamation purposes. Detecting these tampered images is an important task and one we try to tackle. In this paper, we focus on the methods to detect if an image has been tampered with using both Deep Learning and Image transformation methods and comparing the performances and robustness of each method. We then attempt to identify the tampered area of the image and predict the corresponding mask. Bas…
University of Shanghai for Science and Technology（上海理工大学）；Fudan University（复旦大学）
Visual noise is often regarded as a disturbance in image quality, whereas it can also provide a crucial clue for image-based forensic tasks. Conventionally, noise is assumed to comprise an additive Gaussian model to be estimated and then used to reveal anomalies. However, for real sensor noise, it should be modeled as signal-dependent noise (SDN). In this work, we apply SDN to splicing forgery localization tasks. Through statistical analysis of the SDN model, we assume that noise can be modeled as a Gaussian approximation for a certain brightness and propose a likelihood model for a noise leve…
Universiti Malaysia Terengganu（马来西亚登嘉楼大学）；Universiti Tecknologi Malaysia（马来西亚工艺大学）
Most of researches on image forensics have been mainly focused on detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms are not perfect, and accordingly the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection req…
University of Padova（帕多瓦大学）；Politecnico di Milano（米兰理工大学）
Forgery operations on video contents are nowadays within the reach of anyone, thanks to the availability of powerful and user-friendly editing software. Integrity verification and authentication of videos represent a major interest in both journalism (e.g., fake news debunking) and legal environments dealing with digital evidence (e.g., a court of law). While several strategies and different forensics traces have been proposed in recent years, latest solutions aim at increasing the accuracy by combining multiple detectors and features. This paper presents a video forgery localization framework…
Mayachitra Inc（Mayachitra公司）；University of California, Riverside（加州大学河滨分校）；Naval Air Warfare Center Weapons Division（海军空战中心武器部）；Mayachitra Inc., Santa Barbara, California, USA（Mayachitra公司（加利福尼亚州圣巴巴拉））；University of California, Riverside, California, USA（加州大学河滨分校（加利福尼亚州））；Naval Air Warfare Center Weapons Division, China Lake, California, USA（海军空战中心武器部（加利福尼亚州中国湖））
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimen…
Image splicing is a common form of image forgery. Such alterations may leave no visual clues of tampering. In recent works camera characteristics consistency across the image has been used to establish the authenticity and integrity of digital images. Such constant camera characteristic properties are inherent from camera manufacturing processes and are unique. The majority of digital cameras are equipped with spherical lens and this introduces radial distortions on images. This aberration is often disturbed and fails to be consistent across the image, when an image is spliced. This paper desc…
Detection and Localization of Multiple Image Splicing Using MobileNet V1
Symbiosis Institute of Technology, Symbiosis International (Deemed University)（共生国际（ deemed）大学共生技术学院）；Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University)（共生国际（ deemed）大学共生应用人工智能中心）
Civil aviation college, Shenyang Aerospace University（沈阳航空航天大学民航学院）；Criminal Investigation Police University of China（中国刑事警察学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）
Imaging is a sophisticated process combining a plenty of photovoltaic conversions, which lead to some spectral signatures beyond visual perception in the final images. Any manipulation against an original image will destroy these signatures and inevitably leave some traces in the final forgery. Therefore we present a novel optic-physical method to discriminate splicing edges from natural edges in a tampered image. First, we transform the forensic image from RGB into color space of S and o1o2. Then on the assumption of Dichromatic Reflection Model, edges in the image are discovered by composite…
Videos are prone to tampering attacks that alter the meaning and deceive the audience. Previous video forgery detection schemes find tiny clues to locate the tampered areas. However, attackers can successfully evade supervision by destroying such clues using video compression or blurring. This paper proposes a video watermarking network for tampering localization. We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask. The perturbation made by watermark embedding is close to imperceptible. Considering that there is no off-the-shelf different…
Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络空间科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University（深圳广东省智能信息处理重点实验室）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
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 …
Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University（广东智能信息处理重点实验室、电子与信息工程学院）
Pixel-Inconsistency Modeling for Image Manipulation Localization
Nanyang Technology University（南洋理工大学）；School of Electrical and Electronic Engineering（电子电气工程学院）；Rapid-Rich Object Search (ROSE) Lab（快速富对象搜索实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Sun Yat-sen University（中山大学）；City University of Hong Kong（香港城市大学）；University of Campinas（坎皮纳斯大学）；Institute of Computing（计算机研究所）；Artificial Intelligence Laboratory (Recod.ai)（人工智能实验室（Recod.ai））
UVL2: A Unified Framework for Video Tampering Localization
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
University of Macau（澳门大学）；Macau University of Science and Technology（澳门科技大学）
With the continuous research on Deepfake forensics, recent studies have attempted to provide the fine-grained localization of forgeries, in addition to the coarse classification at the video-level. However, the detection and localization performance of existing Deepfake forensic methods still have plenty of room for further improvement. In this work, we propose a Spatial-Temporal Deepfake Detection and Localization (ST-DDL) network that simultaneously explores spatial and temporal features for detecting and localizing forged regions. Specifically, we design a new Anchor-Mesh Motion (AMM) algor…
