Gang Cao

Kun Guo, Haochen Zhu, Gang Cao

Powerful manipulation techniques have made digital image forgeries be easily created and widespread without leaving visual anomalies. The blind localization of tampered regions becomes quite significant for image forensics. In this paper, we propose an effective image tampering localization network (EITLNet) based on a two-branch enhanced transformer encoder with attention-based feature fusion. Specifically, a feature enhancement module is designed to enhance the feature representation ability of the transformer encoder. The features extracted from RGB and noise streams are fused effectively by the coordinate attention-based fusion module at multiple scales. Extensive experimental results verify that the proposed scheme achieves the state-of-the-art generalization ability and robustness in various benchmark datasets. Code will be public at https://github.com/multimediaFor/EITLNet.

Haochen Zhu, Gang Cao, Xianglin Huang

Blind detection of the forged regions in digital images is an effective authentication means to counter the malicious use of local image editing techniques. Existing encoder-decoder forensic networks overlook the fact that detecting complex and subtle tampered regions typically requires more feedback information. In this paper, we propose a Progressive FeedbACk-enhanced Transformer (ProFact) network to achieve coarse-to-fine image forgery localization. Specifically, the coarse localization map generated by an initial branch network is adaptively fed back to the early transformer encoder layers, which can enhance the representation of positive features while suppressing interference factors. The cascaded transformer network, combined with a contextual spatial pyramid module, is designed to refine discriminative forensic features for improving the forgery localization accuracy and reliability. Furthermore, we present an effective strategy to automatically generate large-scale forged image samples close to real-world forensic scenarios, especially in realistic and coherent processing. Leveraging on such samples, a progressive and cost-effective two-stage training protocol is applied to the ProFact network. The extensive experimental results on nine public forensic datasets show that our proposed localizer greatly outperforms the state-of-the-art on the generalization ability and robustness of image forgery localization. Code will be publicly available at https://github.com/multimediaFor/ProFact.

Man Lin, Gang Cao, Zijie Lou

Digital video splicing has become easy and ubiquitous. Malicious users copy some regions of a video and paste them to another video for creating realistic forgeries. It is significant to blindly detect such forgery regions in videos. In this paper, a spatio-temporal co-attention fusion network (SCFNet) is proposed for video splicing localization. Specifically, a three-stream network is used as an encoder to capture manipulation traces across multiple frames. The deep interaction and fusion of spatio-temporal forensic features are achieved by the novel parallel and cross co-attention fusion modules. A lightweight multilayer perceptron (MLP) decoder is adopted to yield a pixel-level tampering localization map. A new large-scale video splicing dataset is created for training the SCFNet. Extensive tests on benchmark datasets show that the localization and generalization performances of our SCFNet outperform the state-of-the-art. Code and datasets will be available at https://github.com/multimediaFor/SCFNet.

Kun Guo, Gang Cao, Zijie Lou et al.

Current image tampering localization methods primarily rely on Convolutional Neural Networks (CNNs) and Transformers. While CNNs suffer from limited local receptive fields, Transformers offer global context modeling at the expense of quadratic computational complexity. Recently, the state space model Mamba has emerged as a competitive alternative, enabling linear-complexity global dependency modeling. Inspired by it, we propose a lightweight and effective FORensic network based on vision MAmba (ForMa) for blind image tampering localization. Firstly, ForMa captures multi-scale global features that achieves efficient global dependency modeling through linear complexity. Then the pixel-wise localization map is generated by a lightweight decoder, which employs a parameter-free pixel shuffle layer for upsampling. Additionally, a noise-assisted decoding strategy is proposed to integrate complementary manipulation traces from tampered images, boosting decoder sensitivity to forgery cues. Experimental results on 10 standard datasets demonstrate that ForMa achieves state-of-the-art generalization ability and robustness, while maintaining the lowest computational complexity. Code is available at https://github.com/multimediaFor/ForMa.

Zijie Lou, Gang Cao, Kun Guo et al.

Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, we propose LoMa, a novel image forgery localization method that leverages the selective SSMs. Specifically, LoMa initially employs atrous selective scan to traverse the spatial domain and convert the tampered image into ordered patch sequences, and subsequently applies multi-directional state space modeling. In addition, an auxiliary convolutional branch is introduced to enhance local feature extraction. Extensive experimental results validate the superiority of LoMa over CNN-based and Transformer-based state-of-the-arts. To our best knowledge, this is the first image forgery localization model constructed based on the SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based forgery localization models. Code is available at https://github.com/multimediaFor/LoMa.

Zijie Lou, Gang Cao, Man Lin

Deep video inpainting is typically used as malicious manipulation to remove important objects for creating fake videos. It is significant to identify the inpainted regions blindly. This letter proposes a simple yet effective forensic scheme for Video Inpainting LOcalization with ContrAstive Learning (ViLocal). Specifically, a 3D Uniformer encoder is applied to the video noise residual for learning effective spatiotemporal forensic features. To enhance the discriminative power, supervised contrastive learning is adopted to capture the local inconsistency of inpainted videos through attracting/repelling the positive/negative pristine and forged pixel pairs. A pixel-wise inpainting localization map is yielded by a lightweight convolution decoder with a specialized two-stage training strategy. To prepare enough training samples, we build a video object segmentation dataset of 2500 videos with pixel-level annotations per frame. Extensive experimental results validate the superiority of ViLocal over state-of-the-arts. Code and dataset will be available at https://github.com/multimediaFor/ViLocal.

Zijie Lou, Gang Cao, Kun Guo et al.

Image forgery localization, which aims to segment tampered regions in an image, is a fundamental yet challenging digital forensic task. While some deep learning-based forensic methods have achieved impressive results, they directly learn pixel-to-label mappings without fully exploiting the relationship between pixels in the feature space. To address such deficiency, we propose a Multi-view Pixel-wise Contrastive algorithm (MPC) for image forgery localization. Specifically, we first pre-train the backbone network with the supervised contrastive loss to model pixel relationships from the perspectives of within-image, cross-scale and cross-modality. That is aimed at increasing intra-class compactness and inter-class separability. Then the localization head is fine-tuned using the cross-entropy loss, resulting in a better pixel localizer. The MPC is trained on three different scale training datasets to make a comprehensive and fair comparison with existing image forgery localization algorithms. Extensive experiments on the small, medium and large scale training datasets show that the proposed MPC achieves higher generalization performance and robustness against post-processing than the state-of-the-arts. Code will be available at https://github.com/multimediaFor/MPC.

Xiaolong Wei, LiFang Yang, Xianglin Huang et al.

At present, attention mechanism has been widely applied to the fields of deep learning models. Structural models that based on attention mechanism can not only record the relationships between features position, but also can measure the importance of different features based on their weights. By establishing dynamically weighted parameters for choosing relevant and irrelevant features, the key information can be strengthened, and the irrelevant information can be weakened. Therefore, the efficiency of deep learning algorithms can be significantly elevated and improved. Although transformers have been performed very well in many fields including reinforcement learning, there are still many problems and applications can be solved and made with transformers within this area. MARL (known as Multi-Agent Reinforcement Learning) can be recognized as a set of independent agents trying to adapt and learn through their way to reach the goal. In order to emphasize the relationship between each MDP decision in a certain time period, we applied the hierarchical coding method and validated the effectiveness of this method. This paper proposed a hierarchical transformers MADDPG based on RNN which we call it Hierarchical RNNs-Based Transformers MADDPG(HRTMADDPG). It consists of a lower level encoder based on RNNs that encodes multiple step sizes in each time sequence, and it also consists of an upper sequence level encoder based on transformer for learning the correlations between multiple sequences so that we can capture the causal relationship between sub-time sequences and make HRTMADDPG more efficient.

Pengpeng Yang, Rongrong Ni, Yao Zhao et al.

Contrast enhancement (CE) forensics techniques have always been of great interest for image forensics community, as they can be an effective tool for recovering image history and identifying tampered images. Although several CE forensic algorithms have been proposed, their accuracy and robustness against some kinds of processing are still unsatisfactory. In order to attenuate such deficiency, in this paper we propose a new framework based on dual-domain fusion convolutional neural network to fuse the features of pixel and histogram domains for CE forensics. Specifically, we first present a pixel-domain convolutional neural network (P-CNN) to automatically capture the patterns of contrast-enhanced images in the pixel domain. Then, we present a histogram-domain convolutional neural network (H-CNN) to extract the features in the histogram domain. The feature representations of pixel and histogram domains are fused and fed into two fully connected layers for the classification of contrast-enhanced images. Experimental results show that the proposed method achieve better performance and is robust against pre-JPEG compression and anti-forensics attacks. In addition, a strategy for performance improvement of CNN-based forensics is explored, which could provide guidance for the design of CNN-based forensics tools.

Gang Cao, Antao Zhou, Xianglin Huang et al.

Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.

Pengpeng Yang, Rongrong Ni, Yao Zhao et al.

Contrast enhancement (CE) forensics has always been ofconcern to image forensics community. It can provide aneffective tool for recovering image history and identifyingtampered images. Although several CE forensic algorithmshave been proposed, their robustness against some processingis still unsatisfactory, such as JPEG compression and anti-forensic attacks. In order to attenuate such deficiency, inthis paper we first present a discriminability analysis of CEforensics in pixel and gray level histogram domains. Then, insuch two domains, two end-to-end methods based on convo-lutional neural networks (P-CNN, H-CNN) are proposed toachieve robust CE forensics against pre-JPEG compressionand anti-forensics attacks. Experimental results show that theproposed methods achieve much better performance than thestate-of-the-art schemes for CE detection in the case of noother operation and comparable performance when pre-JPEGcompression and anti-foresics attacks is used.

Gang Cao, Huawei Tian, Lifang Yu et al.

In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down-sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.

Gang Cao, Lihui Huang, Huawei Tian et al.

As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results.