arXiv subjects

Changsheng Chen

Publications and source records attributed to Changsheng Chen.

30 records · Page 2Linked to original sources

arXiv2023

Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.

cs.CV↗

arXiv2023

Visual Prompt Flexible-Modal Face Anti-Spoofing

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS~\cite{yu2023flexible} has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose \textbf{V}isual \textbf{P}rompt flexible-modal \textbf{FAS} (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4\% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training.

cs.CV↗

arXiv2022

Forensicability Assessment of Questioned Images in Recapturing Detection

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency.

cs.CV↗

arXiv2021

Domain Generalization for Document Authentication against Practical Recapturing Attacks

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision threshold.

cs.MM↗

arXiv2021

Deep Learning-based Forgery Attack on Document Images

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text editing algorithms which alter the textual information of an image . In this work, we present a document forgery algorithm to edit practical document images. To achieve this goal, the limitations of existing text editing algorithms towards complicated characters and complex background are addressed by a set of network design strategies. First, the unnecessary confusion in the supervision data is avoided by disentangling the textual and background information in the source images. Second, to capture the structure of some complicated components, the text skeleton is provided as auxiliary information and the continuity in texture is considered explicitly in the loss function. Third, the forgery traces induced by the text editing operation are mitigated by some post-processing operations which consider the distortions from the print-and-scan channel. Quantitative comparisons of the proposed method and the exiting approach have shown the advantages of our design by reducing the about 2/3 reconstruction error measured in MSE, improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21, respectively. Qualitative experiments have confirmed that the reconstruction results of the proposed method are visually better than the existing approach. More importantly, we have demonstrated the performance of the proposed document forgery algorithm under a practical scenario where an attacker is able to alter the textual information in an identity document using only one sample in the target domain. The forged-and-recaptured samples created by the proposed text editing attack and recapturing operation have successfully fooled some existing document authentication systems.

cs.MM↗

arXiv2020

DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i.e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). In particular, we model the behavior of exploring face-spoofing-related information from image sub-patches by leveraging deep reinforcement learning. We further introduce a recurrent mechanism to learn representations of local information sequentially from the explored sub-patches with an RNN. Finally, for the classification purpose, we fuse the local information with the global one, which can be learned from the original input image through a CNN. Moreover, we conduct extensive experiments, including ablation study and visualization analysis, to evaluate our proposed framework on various public databases. The experiment results show that our method can generally achieve state-of-the-art performance among all scenarios, demonstrating its effectiveness.

cs.CV↗

arXiv2020

Muon Spin Relaxation and fluctuating magnetism in the pseudogap phase of YBa$_{2}$Cu$_{3}$O$_{y}$

We report results of a muon spin relaxation study of slow magnetic fluctuations in the pseudogap phase of underdoped single-crystalline YBa$_{2}$Cu$_{3}$O$_{y}$, $y = 6.77$ and 6.83. The dependence of the dynamic muon spin relaxation rate on applied magnetic field yields the rms magnitude~$B\mathrm{_{loc}^{rms}}$ and correlation time~$τ_c$ of fluctuating local fields at muon sites. The observed relaxation rates do not decrease with decreasing temperature~$T$ below the pseudogap onset at $T^\ast$, as would be expected for a conventional magnetic transition; both $B\mathrm{_{loc}^{rms}}$ and $τ_c$ are roughly constant in the pseudogap phase down to the superconducting transition. Corresponding NMR relaxation rates are estimated to be too small to be observable. Our results put strong constraints on theories of the anomalous pseudogap magnetism in YBa$_{2}$Cu$_{3}$O$_{y}$.

cond-mat.str-el↗

arXiv2020

Detection of Information Hiding at Anti-Copying 2D Barcodes

This paper concerns the problem of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes are either heuristicbased or Machine Learning (ML) based. The key limitation of prior heuristics-based schemes is that they do not answer the fundamental question of why the information hidden at a 2D barcode can be detected. The key limitation of prior MLbased information schemes is that they lack robustness because a printed 2D barcode is very much environmentally dependent, and thus an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes at the existing anti-copying 2D barcodes. The first scheme is to directly use the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred as to the Pixel Distance Based Detection (PDBD) scheme. The second scheme is first to calculate the variance of the raw signal and the covariance between the recovered signal and the raw signal, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred as to the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced IC attacks to evaluate the security of two existing anti-copying 2D barcodes. We implemented our schemes and conducted extensive performance comparison between our schemes and prior schemes under different capturing devices, such as a scanner and a camera phone. Our experimental results show that the PVBD scheme can correctly detect the existence of the hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the probability of successfully attacking of our IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.

cs.CR↗

arXiv2020

Low-Cost Anti-Copying 2D Barcode by Exploiting Channel Noise Characteristics

In this paper, for overcoming the drawbacks of the prior approaches, such as low generality, high cost, and high overhead, we propose a Low-Cost Anti-Copying (LCAC) 2D barcode by exploiting the difference between the noise characteristics of legal and illegal channels. An embedding strategy is proposed, and for a variant of it, we also make the corresponding analysis. For accurately evaluating the performance of our approach, a theoretical model of the noise in an illegal channel is established by using a generalized Gaussian distribution. By comparing with the experimental results based on various printers, scanners, and a mobile phone, it can be found that the sample histogram and curve fitting of the theoretical model match well, so it can be concluded that the theoretical model works well. For evaluating the security of the proposed LCAC code, besides the direct-copying (DC) attack, the improved version, which is the synthesized-copying (SC) attack, is also considered in this paper. Based on the theoretical model, we build a prediction function to optimize the parameters of our approach. The parameters optimization incorporates the covertness requirement, the robustness requirement and a tradeoff between the production cost and the cost of illegally-copying attacks together. The experimental results show that the proposed LCAC code with two printers and two scanners can detect the DC attack effectively and resist the SC attack up to the access of 14 legal copies.

cs.CR↗

arXiv2019

Mitigation of Cascading Outages Using a Dynamic Interaction Graph-Based Optimal Power Flow Model

Online decision support for effective mitigation actions against propagating cascading outages in a power grid still poses a big challenge to grid operators. This paper proposes an online mitigation strategy against cascading outages using an optimal power flow model based on a Dynamic Interaction Graph. For a given power grid, its interaction graph, also called an interaction model, was proposed in recent literature, which is composed of components and links that have large contributions to outage propagation. Differing from a conventional interaction graph, a Dynamic Interaction Graph can adaptively update its parameters with changes of grid topology and hence is more accurate in online identification of the most vulnerable transmission lines and likely outage propagation paths for mitigation. Further, the paper introduces an optimal power flow model based on the proposed Dynamic Interaction Graph for determining the optimal control strategy to maintain transfer margins of vulnerable transmission lines and effectively mitigate outage propagation. The numerical results on the IEEE 118-bus system demonstrate the effectiveness of the proposed models and associated control strategy.

eess.SY↗

arXiv2019

Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing

Face Anti-Spoofing (FAS) is significant for the security of face recognition systems. Convolutional Neural Networks (CNNs) have been introduced to the field of the FAS and have achieved competitive performance. However, CNN-based methods are vulnerable to the adversarial attack. Attackers could generate adversarial-spoofing examples to circumvent a CNN-based face liveness detector. Studies about the transferability of the adversarial attack reveal that utilizing handcrafted feature-based methods could improve security in a system-level. Therefore, handcrafted feature-based methods are worth our exploration. In this paper, we introduce the deep forest, which is proposed as an alternative towards CNNs by Zhou et al., in the problem of the FAS. To the best of our knowledge, this is the first attempt at exploiting the deep forest in the problem of FAS. Moreover, we propose to re-devise the representation constructing by using LBP descriptors rather than the Grained-Scanning Mechanism in the original scheme. Our method achieves competitive results. On the benchmark database IDIAP REPLAY-ATTACK, 0\% Equal Error Rate (EER) is achieved. This work provides a competitive option in a fusing scheme for improving system-level security and offers important ideas to those who want to explore methods besides CNNs.

cs.CV↗

arXiv2015

A Systematic Scheme for Measuring the Performance of the Display-Camera Channel

Display-camera communication has become a promising direction in both computer vision and wireless communication communities. However, the consistency of the channel measurement is an open issue since precise calibration of the experimental setting has not been fully studied in the literatures. This paper focuses on establishing a scheme for precise calibration of the display-camera channel performance. To guarantee high consistency of the experiment, we propose an accurate measurement scheme for the geometric parameters, and identify some unstable channel factors, e.g., Moire effect, rolling shutter effect, blocking artifacts, inconsistency in auto-focus, trembling and vibration. In the experiment, we first define the consistency criteria according to the error-prone region in bit error rate (BER) plots of the channel measurements. It is demonstrated that the consistency of the experimental result can be improved by the proposed precise calibration scheme.

cs.MM↗