Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion

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

Effective Image Tampering Localization via Semantic Segmentation Network

With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low accuracy and robustness. Note that the tampered regions are typically semantic objects, in this letter we propose an effective image tampering localization scheme based on deep semantic segmentation network. ConvNeXt network is used as an encoder to learn better feature representation. The multi-scale features are then fused by Upernet decoder for achieving better locating capability. Combined loss and effective data augmentation are adopted to ensure effective model training. Extensive experimental results confirm that localization performance of our proposed scheme outperforms other state-of-the-art ones

Progressive Feedback-Enhanced Transformer for Image Forgery Localization

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 for enhancing 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

The averaged potential gradient approach to model the rejection of electrolyte solutions using nanofiltration: Model development and assessment for highly concentrated feed solutions

International audienceSome of the recent publications in nanofiltration modelling converge on the importance of dielectric effects and numerous models have been developed in order to take them into account. However several works reported lately in the literature suggest a screening of image charges effect at high electrolyte concentration and the predominance of the Born effect, due to the change of dielectric constant inside the confined nanopore regarding that of the feed solution. In pursuit of an exhaustive and simple model for nanofiltration, a new approach is developed that account for both dielectric phenomena. Based on the Steric, Electric and Dielectric Exclusion (SEDE), the introduction of an average potential gradient approximation is shown to greatly improve the computational performance of the model without being detrimental to its predictive accuracy. The results obtained with this simplified model (SEDE-APG) are compared to the original SEDE model and an excellent agreement is obtained even in the case of electrolyte mixtures. Ultimately this model is confronted to experimental data of separation obtained for moderately to highly concentrated feed flows and exhibits promising result

Computation of the hindrance factor for the diffusion for nanoconfined ions: molecular dynamics simulations versus continuum-based models

Special Issue: Thermodynamics 2011 ConferenceInternational audienceWe report the self-diffusion coefficients and hindrance factor for the diffusion of ions into cylindrical hydrophilic silica nanopores (hydrated silica) determined from molecular dynamics (MD) simulations. We make a comparison with the hindered diffusion coefficients used in continuum-based models of nanofiltration (NF). Hindrance factors for diffusion estimated from the macroscopic hydrodynamic theory were found to be in fair quantitative agreement with MD simulations for a protonated pore, but they strongly overestimate diffusion inside a deprotonated pore

How do East African Communities Cope with the Impacts of Prosopis juliflora (Mesquite) Invasion? A Review

Prosopis juliflora is an evergreen invasive plant native to South America, the Caribbean, and Central America. The plant is well adapted to harsh environmental conditions. As a result, it has spread to most arid and semi-arid areas of the world causing both positive and negative impacts. This study reviewed the adaptation/coping strategies adopted by East African communities as a result of the invasion by the plant. The review results showed that East African communities cope by using the plant for human food and animal feed, leasing the infested land, renting land from uninvaded areas, clearing the plant from farming, grazing land, waterways, paths and homesteads, and using it as fuel in form of firewood and charcoal among others. The communities living in the infested areas now almost entirely depend on the plant for livelihood. Some of the employed adaptations/ coping strategies were found to be inadequate and to have negative environmental impacts. In order to enhance the adaptations/ coping strategies, we recommend commercialization of the plant’s seed for animal feed and human food production, sensitization of the communities on the medicinal use of the plant and that programs to manage the plant should take into account the adaptations the communities have developed over time to avoid negative impacts on the communities’ livelihoods. Keywords: Prosopis juliflora; Coping strategies; East Africa; Invasion; Po

Group Network Hawkes Process

In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Hawkes process (GNHP) model whose network structure is observed and fixed. In particular, we introduce a latent group structure among individuals to account for the heterogeneous user-specific characteristics. A maximum likelihood approach is proposed to simultaneously cluster individuals in the network and estimate model parameters. A fast EM algorithm is subsequently developed by utilizing the branching representation of the proposed GNHP model. Theoretical properties of the resulting estimators of group memberships and model parameters are investigated under both settings when the number of latent groups $G$ is over-specified or correctly specified. A data-driven criterion that can consistently identify the true $G$ under mild conditions is derived. Extensive simulation studies and an application to a data set collected from Sina Weibo are used to illustrate the effectiveness of the proposed methodology.Comment: 35 page

Knowledge Editing for Large Language Models: A Survey

Large language models (LLMs) have recently transformed both the academic and industrial landscapes due to their remarkable capacity to understand, analyze, and generate texts based on their vast knowledge and reasoning ability. Nevertheless, one major drawback of LLMs is their substantial computational cost for pre-training due to their unprecedented amounts of parameters. The disadvantage is exacerbated when new knowledge frequently needs to be introduced into the pre-trained model. Therefore, it is imperative to develop effective and efficient techniques to update pre-trained LLMs. Traditional methods encode new knowledge in pre-trained LLMs through direct fine-tuning. However, naively re-training LLMs can be computationally intensive and risks degenerating valuable pre-trained knowledge irrelevant to the update in the model. Recently, Knowledge-based Model Editing (KME) has attracted increasing attention, which aims to precisely modify the LLMs to incorporate specific knowledge, without negatively influencing other irrelevant knowledge. In this survey, we aim to provide a comprehensive and in-depth overview of recent advances in the field of KME. We first introduce a general formulation of KME to encompass different KME strategies. Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category. Moreover, representative metrics, datasets, and applications of KME are introduced accordingly. Finally, we provide an in-depth analysis regarding the practicality and remaining challenges of KME and suggest promising research directions for further advancement in this field.Comment: 33 page