SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection

• URL: http://arxiv.org/abs/2002.06735v2
• Date: Wed, 19 Feb 2020 16:36:31 GMT
• Title: SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection
• Authors: Alexandru \c{S}erban, George Ila\c{s}, George-Cosmin Poru\c{s}niuc
• Abstract summary: The counterfeit goods trade represents nowadays more than 3.3% of the whole world trade. This paper presents the design and early stage development of a novel counterfeit goods detection platform.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The counterfeit goods trade represents nowadays more than 3.3% of the whole world trade and thus it's a problem that needs now more than ever a lot of attention and a reliable solution that would reduce the negative impact it has over the modern society. This paper presents the design and early stage development of a novel counterfeit goods detection platform that makes use of the outstsanding learning capabilities of the classical VGG16 convolutional model trained through the process of "transfer learning" and a multi-stage fake detection procedure that proved to be not only reliable but also very robust in the experiments we have conducted so far using an image dataset of various goods which we gathered ourselves.

Related papers

• Credit Card Fraud Detection Using Advanced Transformer Model [15.34892016767672]
  This study focuses on innovative applications of the latest Transformer models for more robust and precise fraud detection. We meticulously processed the data sources, balancing the dataset to address the issue of data sparsity significantly. We conducted performance comparisons with several widely adopted models, including Support Vector Machine (SVM), Random Forest, Neural Network, and Logistic Regression.
  arXiv  Detail & Related papers  (2024-06-06T04:12:57Z)
• A New Transformation Approach for Uplift Modeling with Binary Outcome [7.828300476533517]
  Uplift modeling is a machine learning technique that predicts the gain from performing some action with respect to not taking it. In this paper, we design a novel transformed outcome for the case of the binary target variable and unlock the full value of the samples with zero outcome. Our new approach has already been applied to precision marketing in a China nation-wide financial holdings group.
  arXiv  Detail & Related papers  (2023-10-09T09:17:52Z)
• DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues [32.045504965382015]
  Current deepfake detection models can generally recognize forgery images by training on a large dataset. The accuracy of detection models degrades significantly on images generated by new deepfake methods due to the difference in data distribution. We present a novel incremental learning framework that improves the generalization of deepfake detection models.
  arXiv  Detail & Related papers  (2023-09-18T07:02:26Z)
• Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection [64.65563422852568]
  We improve the challenging monocular 3D object detection problem with a general semi-supervised framework. We introduce a novel, simple, yet effective Augment and Criticize' framework that explores abundant informative samples from unlabeled data. The two new detectors, dubbed 3DSeMo_DLE and 3DSeMo_FLEX, achieve state-of-the-art results with remarkable improvements for over 3.5% AP_3D/BEV (Easy) on KITTI.
  arXiv  Detail & Related papers  (2023-03-20T16:28:15Z)
• SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection [108.57862846523858]
  We revisit the self-supervised multi-task learning framework, proposing several updates to the original method. We modernize the 3D convolutional backbone by introducing multi-head self-attention modules. In our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps.
  arXiv  Detail & Related papers  (2022-07-16T19:25:41Z)
• What Stops Learning-based 3D Registration from Working in the Real World? [53.68326201131434]
  This work identifies the sources of 3D point cloud registration failures, analyze the reasons behind them, and propose solutions. Ultimately, this translates to a best-practice 3D registration network (BPNet), constituting the first learning-based method able to handle previously-unseen objects in real-world data. Our model generalizes to real data without any fine-tuning, reaching an accuracy of up to 67% on point clouds of unseen objects obtained with a commercial sensor.
  arXiv  Detail & Related papers  (2021-11-19T19:24:27Z)
• An Effective and Robust Detector for Logo Detection [58.448716977297565]
  Some attackers fool the well-trained logo detection model for infringement. A novel logo detector based on the mechanism of looking and thinking twice is proposed in this paper. We extend detectoRS algorithm to a cascade schema with an equalization loss function, multi-scale transformations, and adversarial data augmentation.
  arXiv  Detail & Related papers  (2021-08-01T10:17:53Z)
• Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection [0.0]
  We present a novel method for detecting fake news by fusing multimodal features derived from textual and visual data. Experimental results showed that our approach performs better than the current state-of-the-art method on a public Twitter dataset by 3.1% accuracy.
  arXiv  Detail & Related papers  (2021-04-23T08:47:54Z)
• Reducing Representation Drift in Online Continual Learning [87.71558506591937]
  We study the online continual learning paradigm, where agents must learn from a changing distribution with constrained memory and compute. In this work we instead focus on the change in representations of previously observed data due to the introduction of previously unobserved class samples in the incoming data stream.
  arXiv  Detail & Related papers  (2021-04-11T15:19:30Z)
• Connecting the Dots Between Fact Verification and Fake News Detection [21.564628184287173]
  We propose a simple yet effective approach to connect the dots between fact verification and fake news detection. Our approach makes use of the recent success of fact verification models and enables zero-shot fake news detection.
  arXiv  Detail & Related papers  (2020-10-11T09:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.