RealStats detects fake images rigorously, combining multiple detectors for robustness.
problem Detecting AI-generated images remains challenging due to evolving generative models.
method Combines training-free statistics to compute p-values and aggregate them for a unified real-image distribution.
result Framework produces interpretable probability scores for robust fake image detection.
Generative Adversarial Networks optimize model parameters for image matching.
problem Optimizing model parameters for accurate image matching.
method Model-Assisted Generative Adversarial Network (GAN) to produce fake images matching true images.
result Best match model parameter values can minimize bias in image recognition.
SAFE detects fake news by analyzing text and image similarities.
problem Detecting fake news with less focus on text-image similarity.
method SAFE uses neural networks to extract text and visual features, then learns their relationship to predict fake news.
result SAFE effectively recognizes fake news based on text, images, or mismatches.
Simple method detects deepfakes with few labeled samples.
problem Automated detection of AI-generated fake images.
method Frequency domain analysis followed by basic classifier.
result Achieved 100% accuracy in high-resolution face images with minimal training data.
HMN detects fake faces using memory networks and visual cues.
problem Detecting fake faces from unseen manipulation types.
method Hierarchical Memory Network (HMN) architecture.
result Superior performance in fake and fraudulent face detection.
Paper explores generalization of GAN image forensics methods.
problem Ensuring forensics models detect GAN-generated images across new types.
method Preprocessed images training for a forensic CNN model.
result Proposed method effectively detects GAN-generated images.
We introduce a novel approach for training adversarial models by replacing the discriminator score with a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. W…
This note provides an easy construction of fake octagons.
problem Existence and construction of fake octagons.
method Elementary cut-and-paste surgery to produce infinitely many distinct fake octagons.
result Any iterate of the surgery produces a fake octagon that is different from others in the family.
Classifies fake surfaces up to complexity 5.
problem Classifying fake surfaces for low-dimensional topology.
method Derived properties of fake surfaces, classified up to complexity 5.
result Proved conjectures about fake surfaces up to complexity 5.
NOT learns optimal transport plans, kernel costs improve performance.
problem NOT algorithm learns non-optimal plans with weak quadratic costs.
method Introduced kernel weak quadratic costs to improve NOT's performance.
result Kernel costs provide improved theoretical and practical guarantees.
HDSF detects fake news by learning hierarchical discourse-level structures.
problem Detecting fake news articles with minimal annotated corpora.
method Automatically learns and constructs discourse-level structures for fake and real news articles.
result Real and fake news present substantial differences in hierarchical discourse-level structures.
We study two important concepts in adversarial deep learning---adversarial training and generative adversarial network (GAN). Adversarial training is the technique used to improve the robustness of discriminator by combining adversarial attacker and discriminator in the training phase. GAN is commonly used for image ge…
This study detects fake and automated accounts on Instagram.
problem Fake engagement on Instagram leads to financial loss and wrong audience targeting.
method Two datasets were created and machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines, Neural Networks, and cost-sensitive genetic algorithm were applied.
result 86% accuracy for automated accounts and 96% for fake accounts were achieved.
In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would accou…
New framework investigates how fake features affect model generalization.
problem Model misspecification with fake features and their impact on generalization.
method Non-asymptotic high-probability bound on ridge regression generalization error.
result Trade-off between fake features and optimal ridge parameter.
Paper uses PSM to improve fake news detection generalizability.
problem Confounding variables in fake news features.
method Propensity Score Matching (PSM) to select features.
result Generalizability of fake news detection methods improved significantly.
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different gener…
Repository tackles fake health news in cancer research.
problem Spread of fake health news over the internet.
method Developed comprehensive FakeHealth repository with rich features and detailed explanations.
result Repository helps in understanding and validating health fake news datasets.
Paper proves contractible fake surfaces up to complexity 6 are deformable.
problem Stable Andrews-Curtis conjecture and contractible fake surfaces.
method Induction scheme proving contractibility up to complexity 6.
result Contractible fake surfaces up to complexity 6 are 3-deformable.
MALCOM generates fake comments to fool fake news detectors.
problem Adversaries can manipulate fake news detection models with malicious comments.
method Proposes a novel threat model and develops an adversarial comment generation framework (MALCOM).
result MALCOM can fool fake news detectors 90-94% of the time, depending on the model and dataset.
Paper proposes HGAT for detecting fake news on HIN.
problem Viral spread of fake news causing social harm.
method Hierarchical Graph Attention Network (HGAT) for node representation learning in HIN.
result HGAT outperforms text-based and network-based models.
Study detects fake news in Brazilian Portuguese using machine learning.
problem Spread of misinformation through fake news in Brazilian Portuguese.
method Machine learning, TF-IDF, Word2Vec, classification algorithms.
result High accuracy and F1-Score in identifying fake news.
Improves estimation under model misspecification with fake features.
problem Model misspecification with fake features.
method Proposes a framework to decompose output error into underlying, fake, and missing features.
result Fake features can significantly improve estimation performance, even when not correlated with underlying features.
Paper explores different models for fake news detection.
problem Detecting fake news with various models.
method Used machine learning algorithms and pretrained features like TFIDF, CV, and W2V.
result Developed models for better fake news detection.
Study examines fake news as modern myths using AI.
problem Misinformation and propaganda in fake news.
method Machine learning to generate fake articles.
result Details of fake news generation pipeline.
We apply the recent results of Galkin et al. [GKMS15] to study some geometrical features of Keum's fake projective planes. Among other things, we show that the bicanonical map of Keum's fake projective planes is always an embedding. Moreover, we construct a nonstandard exceptional collection on the unique fake projecti…
In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brough…
We apply the lifting theorem of Searle and the second author to put metrics of almost nonnegative curvature on the fake RP^{6}s of Hirsch and Milnor and on the analogous fake RP^{14}s.
Geometric deep learning detects fake news on social media.
problem Detecting fake news on social media due to lack of context understanding.
method Propagation-based geometric deep learning model.
result Highly accurate fake news detection (92.7% ROC AUC).
In the present article, we provide examples of fake quadrics, that is, minimal complex surfaces of general type with the same numerical invariants as the smooth quadric in $\PP ^3$ which are quotients of the bidisc by an irreducible lattice of automorphisms. Moreover, we list classes of arithmetic lattices over a real …
A fake quadric is a smooth projective surface that has the same rational cohomology as a smooth quadric surface but is not biholomorphic to one. We provide an explicit classification of all irreducible fake quadrics according to the commensurability class of their fundamental group. To accomplish this task, we develop …
Crowdsourced algorithms identify fake news on Twitter.
problem Identifying fake news on social media platforms.
method Evaluation of reputation algorithms on a large dataset of Twitter news.
result Simple crowdsourcing-based algorithms can identify a significant portion of fake news with low false positive rates.
We study quotients Γ\Hn of the n-fold product of the upper half plane H by irreducible and torsion-free lattices Γ<PSL2(R)n with the same Betti numbers as the n-fold product (P1)n of projective lines. Such varieties are called fake products of projective lines…
Survey on fake news identification and mitigation techniques.
problem Proliferation of fake news on social media impacts public opinion.
method Identification and mitigation techniques, focusing on content and user engagement.
result Significant advances in identification and mitigation methods.
In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework. One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them. The other strategy learns formal specifications from the real data and show…
Study benchmarks machine learning models for fake news detection.
problem Dataset bias and performance of fake news detection models.
method Benchmarked different machine learning models on three datasets.
result BERT and similar pre-trained models perform best for fake news detection.
This study improves text summarization and fake news detection using neural models.
problem Improving text summarization and detecting fake news.
method Exploring and comparing different neural models for text summarization, including LSTM-encoder-decoder, pointer-generator networks, and transformers. Applying these models as a feature extractor for fake news detection.
result The proposed neural models enhance text summarization and improve fake news detection accuracy.
New algebraic fundamental groups identified for fake projective planes.
problem Characterizing algebraic fundamental groups of fake projective planes.
method Analysis of complex conjugate pairs and explicit finite étale covers.
result Forty-six distinct isomorphism classes of algebraic fundamental groups.
Framework detects fake news using weak social signals from multiple sources.
problem Lack of annotated data for early fake news detection.
method Jointly uses weak social signals and clean data to train deep neural networks in a meta-learning framework.
result Framework outperforms state-of-the-art baselines for early fake news detection.
Graph model detects fake news by analyzing sentence interactions.
problem Detecting fake news through online media.
method Graph Neural Network-based model for sentence interactions.
result Our model achieves state-of-the-art accuracy on fake news datasets.
GCAN detects fake news on social media with explanations.
problem Detecting fake news on social media with explanations.
method Graph-aware Co-Attention Networks (GCAN).
result GCAN significantly outperforms state-of-the-art methods in accuracy.
Few-step distillation improves T2I models without real images or CFG trade-offs.
problem Challenges in accelerating T2I models with high-resolution and CFG.
method Score identity distillation (SiD) for few-step generation, with adversarial loss and new guidance strategies.
result State-of-the-art performance on SDXL at 1024x1024 resolution, robust to real images absence.
This work introduces an efficient method to sample high-quality images from conditional GANs.
problem Efficient subsampling of images from conditional GANs (cGANs) is challenging.
method Developed a novel conditional density ratio estimation method (cDRE-F-cSP) and rejection sampling scheme (cDR-RS).
result cDR-RS outperforms state-of-the-art methods in both effectiveness and efficiency.
We give a criterion for a projective surface to become a quotient of a fake projective plane. We also give a detailed information on the elliptic fibration of a (2,3)-elliptic surface that is the minimal resolution of a quotient of a fake projective plane. As a consequence, we give a classification of Q-h…
A Triangle Generative Adversarial Network (Δ-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. Δ-GAN consists of four neural networks, two ge…
Study tackles ranking fraud in online platforms by learning robust rankings.
problem Fraudulent fake users manipulate product rankings.
method Developed algorithms for robust ranking in two informational environments.
result Our algorithms converge to optimal rankings, robust to fake users.
A fake wedge is a diagram of spaces K <- A -> C whose double mapping cylinder is contractible. The terminology stems from the special case A = K v C with maps given by the projections. In this paper, we study the homotopy type of the moduli space D(K,C) of fake wedges on K and C. We formulate two conjectures concerning…
Deep learning uses alphabet frequencies to accurately classify fake news.
problem Classifying fake news from trustworthy news.
method Used deep learning algorithms on alphabet frequencies of text without sequence information.
result Achieved high accuracy (85%) in classifying fake news.