Fake data

Author: p | 2025-04-24

Another way to say Fake Data? Synonyms for Fake Data (other words and phrases for Fake Data).

Fake News, Fake Data - fmamfg.org

Fake Filler 2 - A Form Filler for Developers and TestersFake Filler 2 is a form filler add-on for Chrome that is designed to fill all input fields on a page with randomly generated fake data. This powerful extension is a must-have for developers and testers who frequently work with forms, as it eliminates the need for manually entering values in fields.With Fake Filler 2, you can easily fill all inputs with randomly generated names, emails, phone numbers, and more. The extension provides sensible defaults, allowing you to start using it right away without any configuration. This saves you time and effort, especially when dealing with large forms or repetitive data entry tasks.One of the standout features of Fake Filler 2 is its powerful customization options. You can create custom fields to generate specific types of data, giving you full control over the fake information that is filled in. This flexibility makes the extension suitable for a wide range of use cases.Additionally, Fake Filler 2 intelligently ignores CAPTCHA, hidden, disabled, and readonly fields. This ensures that the extension only fills in the relevant input fields, avoiding any potential conflicts or disruptions to the form submission process.Overall, Fake Filler 2 is a highly useful tool for developers and testers who want to streamline their form filling process. Its ability to automatically generate fake data and its customizable features make it a valuable asset in improving productivity and efficiency.Program available in other languagesFake Filler 2 다운로드 [KO]Pobierz Fake Filler 2 [PL]Télécharger Fake Filler 2 [FR]Download do Fake Filler 2 [PT]تنزيل Fake Filler 2 [AR]Скачать Fake Filler 2 [RU]Descargar Fake Filler 2 [ES]下载Fake Filler 2 [ZH]Fake Filler 2 herunterladen [DE]Ladda ner Fake Filler 2 [SV]Download Fake Filler 2 [NL]ดาวน์โหลด Fake Filler 2 [TH]Tải xuống Fake Filler 2 [VI]ダウンロードFake Filler 2 [JA]Unduh Fake Filler

Fake News, Fake Data - Scholastic

Users can customize various aspects of the fake tweets, such as the profile picture, username, tweet content, number of likes and retweets, and timestamp. Is Fake Tweet Creator Reel Maker affiliated with Twitter? No, Fake Tweet Creator Reel Maker is not affiliated with Twitter. It is a third-party tool developed for entertainment purposes only. Are there any restrictions on how Fake Tweet Creator Reel Maker can be used? Users should use Fake Tweet Creator Reel Maker responsibly and avoid using it for malicious or deceptive purposes. The tool should only be used for entertainment or satire. Can fake tweets created with Fake Tweet Creator Reel Maker be shared on social media? Yes, users can save the fake tweets generated by Fake Tweet Creator Reel Maker and share them on social media platforms like Twitter, Facebook, and Instagram. Does Fake Tweet Creator Reel Maker store any user data or information? Fake Tweet Creator Reel Maker does not store any user data or information. The tool operates on a session basis, and once the user leaves the site, their data is not retained. Is Fake Tweet Creator Reel Maker safe to use? Yes, Fake Tweet Creator Reel Maker is safe to use. The tool does not require any personal information from users and is designed for creating fake tweets in a fun and harmless manner.

Fake Filler - Fake data generator

Misleading ML algorithms and neural networks can happen easily — a bit of noise to data here and there, and the algorithms start misclassifying things. For those relying on AI and ML services for our future, any misclassification casts a shadow over the development of AI technology. To avoid misclassification, there appeared a new idea to allow neural networks to visualize new patterns like sample train data. Eventually, there appeared the first Generative Adversarial Network, delivering new fake results similar to the original. What does GAN mean? In this article, we’ll provide you with a comprehensive guide to this phenomenon. What is GAN?To better apprehend what Generative Adversarial Networks (GANs) are, let’s break the concept into three elements:Generative means studying a generative model, explaining how data is optically created.Adversarial means that model training takes place in an antagonistic environment.Networks are deep neural networks used for many training objectives. GANs feature two models/categories accountable for uncovering patterns in input data and learning them: Generator and Discriminator.GAN GeneratorIt’s a neural network engaged in fake (plausible) data creation. The Generator’s core goal is to make the Discriminator sort out fake data outputs as real. A GAN has a part accountable for Generator training. It includes a noisy input vector, the Generator net accountable for converting random inputs into data samples, the Discriminator net that categorizes data, and Generator loss that disciplines the Generator if it fails to fool the Discriminator. GAN DiscriminatorIt’s a neural network engaged in identifying real data from the fake one rendered by the Generator. The data stems from two roots: real data samples utilized by the Discriminator as positive examples and fake (plausible) data samples created by the Generator and applied as negative samples during training. For training, the Discriminator binds to 2 loss functions, neglecting the Generator loss and utilizing the Discriminator loss exclusively. The Discriminator loss can impose a sanction on the Discriminator if real data examples are misclassified as fake and vice versa. How Do GANs Work?As a prominent framework for approaching generative AI, a GAN has a very specific workflow.So, how do Generative Adversarial Networks. Another way to say Fake Data? Synonyms for Fake Data (other words and phrases for Fake Data). Fake data regeneration is really annoying for project regeneration. I am testing a blueprint and regenerating the project all the time. Would be really nice an option to don't regenerate the fake data. A version of skip-fake-data with skip-fake-data-regeneration option:

djensenius/fake-data-streamer: Fake data streamer - GitHub

Media. This means that while the file names and directories appeared on the computer, the actual data was lost.This type of behavior is typical of fake drives, where the firmware is programmed to overwrite existing data or not write new data beyond a certain point. Technical Details The drive reported having 4 billion sectors of 512 bytes each, corresponding to its claimed capacity. However, upon closer examination, it was found to contain only 58GB of usable storage. Any data written beyond this limit would not be stored correctly, leading to data loss. This discrepancy arises from firmware manipulation, which tricks the operating system into displaying false capacity information. Identifying Fake Flash DrivesCheck the Specifications Compare the advertised specifications with what is typical for genuine products. If a flash drive claims to offer an unusually large capacity for a very low price, it is likely fake. Familiarize yourself with the average price range for different capacities and brands to better spot anomalies. Examine the Physical Drive Look for signs of tampering or low-quality construction. Genuine high-capacity drives tend to have high quality materials and consistent labeling.Pay attention to the packaging as well; authentic products typically come in professionally designed and printed packaging, whereas counterfeit products might come in generic or poorly printed packaging. Review Seller Reputation Purchase from reputable sellers and avoid listings from unknown or dubious sources. Check reviews and ratings for signs of recurring issues with fake products. Be wary of sellers with a large number of low-cost, high-capacity flash drives and read through the negative reviews for specific mentions of fake products. Assess the Drive Determine if the drive is fake by checking its reported capacity against its actual storage. Use diagnostic tools to understand if the drive is misreporting data.If no data has been written to the drive yet, attempt to format the drive. If there are any discrepancies, such as the drive’s storage suddenly decreasing, or there are errors not allowing the drive to format, it is likely fake. Handling Data Recovery from Fake Flash Drives When dealing with fake flash drives, data recovery can be challenging. Since these drives often do not store data as they claim, recovering lost data may be impossible. Here are some steps to follow: Do Not Write Any New Data to the Drive New data being written to a fake flash drive jeopardizes the integrity of any critical files that may be still intact on the drive. Due to the delicate treatment required to properly handle accessing these types of devices, ensure use of the drive is strictly limited. Any unnecessary access leaves data vulnerable. Document and Report Keep a record of the fake drives you encounter, including details about their purchase source, reported capacity, actual capacity, and any recovery attempts. Report these cases to relevant authorities or platforms to help combat the proliferation of counterfeit products. Communicate with the Customer If you are an IT or computer repair professional, clearly explain the situation to your customer. Let them know

Generating Fake Data for Data Analytics

MACHINE LEARNING DEPLOYMENTPhoto by Markus Winkler from UnsplashThe spread of fake news is unstoppable with the adoption of different social networks. On Twitter, Facebook, Reddit, people take advantage of fake news to spread rumours, win political benefits and click rates.Detecting fake news is critical for a healthy society, and there are multiple different approaches to detect fake news. From a machine learning standpoint, fake news detection is a binary classification problem; hence we can use traditional classification methods or state-of-the-art Neural Networks to deal with this problem.This tutorial will create a natural language processing application from scratch and deploy it on Flask. In the end, you will have a Fake news detection web app running on your local machine. See the teaser here.The tutorial is organized in the following structure:Step1: Load data from Kaggle to Google Colab.Step2: Text preprocessing.Step3: Model training and validation.Step4: Pickle and load model.Step5: Create a Flask APP and a virtual environment.Step6: Add functionalities.Conclusion.Note: The complete notebook is on GitHub.Step1: Load data from Kaggle to Google ColabWell, the most fundamental part of a machine learning project is data. We will use the Fake and real news dataset from Kaggle to build our machine learning model.I wrote a blog about how to download data from Kaggle to Google Colab before. Feel free to follow the steps inside.There are two separate CSV files in the folder, True and False, corresponding to Real and Fake news. Let’s have a look at what the data look like:true = pd.read_csv('True.csv')fake = pd.read_csv('Fake.csv')true.head(3)The first three rows of the TRUE CSV file (Image by the author)Step2: Text preprocessingThe datasets have four columns, but they have no label yet, let’s create labels first. Fake news as label 0 and Real news label 1.true['label'] = 1fake['label'] = 0The datasets are relatively clean and organized. For the sake of training speed, we are using the first 5000 data points in both datasets to build the model. You can also use the complete datasets to get a more comprehensive result.# Combine the sub-datasets in one.frames = [true.loc[:5000][:], fake.loc[:5000][:]]df = pd.concat(frames)df.tail()The combined dataset for training and testing (Image by the author)Let’s also separate features and labels as well as make a copy of the DataFrame for later training.X = df.drop('label', axis=1) y = df['label']# Delete missing datadf = df.dropna()df2 = df.copy()df2.reset_index(inplace=True)Cool! Time for the real text preprocessing, which includes deleting punctuations, lowering all capitalized characters, deleting all stopwords, and

Fake data Archives - Data Colada

Small dataset (composed by 10,000 samples at maximum); a further analysis of some multimedia approaches in order to improve the overall fake news detection performances, also considering misleading images. 3 Fake news detection: an experimental test bed frameworkWe define a Fake News Detection framework for experimental purposes, based on news flow processing and data management, as depicted in Fig. 1. In particular, a preliminary pre-processing stage executes filtering and aggregation operation over the news content, and in addition filtered data are processed by two independent modules: the first one performs natural language processing over data, while the second one performs a multimedia analysis.Fig. 1The overall process at a glanceFull size imageMore in details: Data Ingestion Module. This module takes care of data collection tasks. Data can be highly heterogeneous as well as social network, multimedia and news data. We collect the news text and eventual related contents and images. Pre-processing Module. This component is devoted to the acquisition of the incoming data flow. It performs filtering, data aggregation, data cleaning and some enrichment operations. NLP Processing Module. It performs the crucial task of generating a binary classification of the news articles, i.e., whether they are fake or reliable news. It is split in two sub-modules. The Machine Learning module performs classification using an ad-hoc implemented algorithms after an extensive process of feature extraction and selection TF-IDF based (in order to reduce the number of extracted features). The Deep Learning module classifies data by different engines, after a tuning phase on the vocabulary. It also perform a binary transformation and eventual text padding in order to better analyze the input data. Multimedia Processing Module. This module is tailored for Fake Image Classification through Deep Learning algorithms, using ELA (Error Level Analysis) and CNN. Due to space limitation, we discuss in the following only the details of the deep learning module bases on Google B.E.R.T. framework(Devlin et al., 2019), and the obtained results.3.1 Software architectureThe software implementation of the framework described above is shown in Fig. 2.Fig. 2A fake news detection frameworkFull size imageHerein: the data ingestion block is implemented by using

migratetoflarum/fake-data: Generate fake data for your test forum - GitHub

34(2), 76–81.Article Google Scholar Rubin, V.L., Chen, Y., & Conroy, N.J. (2015). Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1), 1–4.Article Google Scholar Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019). Combating fake news: a survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST), 10(3), 21. Google Scholar Shu, K., Cui, L., Wang, S., Lee, D., & Liu, H. (2019). defend: Explainable fake news detection. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 395–405).Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2018). Fakenewsnet: A data repository with news content, social context and dynamic information for studying fake news on social media. arXiv:1809.01286.Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36.Article Google Scholar Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. In Culpepper et al. (2019). (pp. 312–320).Silva, R.M., Santos, R.L., Almeida, T.A., & Pardo, T.A. (2020). Towards automatically filtering fake news in Portuguese. Expert Systems with Applications, 146, 113199.Article Google Scholar Vosoughi, S., Mohsenvand, M.N., & Roy, D. (2017). Rumor gauge: Predicting the veracity of rumors on twitter. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(4), 1–36.Article Google Scholar Wang, S., & Terano, T. (2015). Detecting rumor patterns in streaming social media. In 2015 IEEE international conference on big data (big data) (pp. 2709–2715). IEEE.Wang, W.Y. (2017). “liar, liar pants on fire”:, A new benchmark dataset for fake news detection. arXiv:1705.00648.Wang, Y., Yang, W., Ma, F., Xu, J., Zhong, B., Deng, Q., & Gao, J. (2020). Weak supervision for fake news detection via reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, (Vol. 34 pp. 516–523).Wu, K., Yang, S., & Zhu, K.Q. (2015). False rumors detection on sina weibo by propagation structures. In 2015 IEEE 31St international conference on data engineering (pp. 651–662). IEEE.Zhou,. Another way to say Fake Data? Synonyms for Fake Data (other words and phrases for Fake Data).

Fake Data and Fake Information: A Treasure Trove for Defenders

= typeof chainId === 'string' ? chainId : String.fromCharCode(chainId); const address = libs.crypto.address({ publicKey }, chain); return verifyAuthData({ publicKey, address, signature }, data);};// Obtaining the signatureconst data = await KeeperWallet.auth({ data: '123' });authValidate(data, { host: data.host, data: '123' }); // trueJS example code { const chain = typeof chainId === 'string' ? chainId : String.fromCharCode(chainId); const address = libs.crypto.address({ publicKey }, chain); return verifyAuthData({ publicKey, address, signature }, data);};// Obtaining the signatureconst data = await KeeperWallet.auth({ data: '123' });authValidate(data, { host: data.host, data: '123' }); // true">import { verifyAuthData, libs } from '@waves/waves-transactions';const authValidate = (signature, data, publicKey, chainId) => { const chain = typeof chainId === 'string' ? chainId : String.fromCharCode(chainId); const address = libs.crypto.address({ publicKey }, chain); return verifyAuthData({ publicKey, address, signature }, data);};// Obtaining the signatureconst data = await KeeperWallet.auth({ data: '123' });authValidate(data, { host: data.host, data: '123' }); // truePython example codeimport axolotl_curve25519 as curveimport pywaves.crypto as cryptoimport base58from urllib.parse import urlparse, parse_qsdef str_with_length(string_data): string_length_bytes = len(string_data).to_bytes(2, byteorder='big') string_bytes = string_data.encode('utf-8') return string_length_bytes + string_bytesdef signed_data(host, data): prefix = 'WavesWalletAuthentication' return str_with_length(prefix) + str_with_length(host) + str_with_length(data)def verify(public_key, signature, message): public_key_bytes = base58.b58decode(public_key) signature_bytes = base58.b58decode(signature) return curve.verifySignature(public_key_bytes, message, signature_bytes) == 0def verifyAddress(public_key, address): public_key_bytes = base58.b58decode(public_key) unhashed_address = chr(1) + str('W') + crypto.hashChain(public_key_bytes)[0:20] address_hash = crypto.hashChain(crypto.str2bytes(unhashed_address))[0:4] address_from_public_key = base58.b58encode(crypto.str2bytes(unhashed_address + address_hash)) return address_from_public_key == addressaddress = '3PCAB4sHXgvtu5NPoen6EXR5yaNbvsEA8Fj'pub_key = '2M25DqL2W4rGFLCFadgATboS8EPqyWAN3DjH12AH5Kdr'signature = '2w7QKSkxKEUwCVhx2VGrt5YiYVtAdoBZ8KQcxuNjGfN6n4fi1bn7PfPTnmdygZ6d87WhSXF1B9hW2pSmP7HucVbh'data_string = '0123456789abc'host_string = 'example.com'message_bytes = signed_data(host_string, data_string)print('Address:', address)print('Public key:', pub_key)print('Signed Data:', message_bytes)print('Real signature:', signature)print('Verified:', verify(pub_key, signature, message_bytes))print('Address verified:', verifyAddress(pub_key, address))fake_signature = '29qWReHU9RXrQdQyXVXVciZarWXu7DXwekyV1zPivkrAzf4VSHb2Aq2FCKgRkKSozHFknKeq99dQaSmkhUDtZWsw'print('Fake signature:', fake_signature)print('Fake signature verification:', verify(pub_key, fake_signature, message_bytes))PHP example codelog('i', 'Address: '. $address);$wk->log('i', 'Public key:' . $pub_key);$wk->log('i', 'Signed Data: ' . $message_bytes);$wk->log('i', 'Real signature: '. $signature);$wk->setPublicKey( $pub_key );$is_address_verified = $address === $wk->getAddress();if ( $is_address_verified === true) $wk->log('s', "Address: Verified: TRUE");else $wk->log('e', "Address: Verified: FALSE");$signature_verified = $wk->verify($wk->base58Decode($signature), $message_bytes);if ( $signature_verified === true) $wk->log('s', "Signature Verified: TRUE");else $wk->log('e', "Signature Verified: FALSE");$fake_signature = '29qWReHU9RXrQdQyXVXVciZarWXu7DXwekyV1zPivkrAzf4VSHb2Aq2FCKgRkKSozHFknKeq99dQaSmkhUDtZWsw';$wk->log('i', 'Fake Signature: '. $fake_signature);$signature_verified = $wk->verify($wk->base58Decode($fake_signature), $message_bytes);if ( $signature_verified === true) $wk->log('e', "Fake Signature Verified: TRUE");else $wk->log('s', "Fake Signature Verified: FALSE");?>">/* * Requires WavesKit by deemru * */require_once __DIR__ . '/vendor/autoload.php';use deemru\WavesKit;function signed_data( $host, $data ){ $prefix = 'WavesWalletAuthentication'; return str_with_length($prefix) . str_with_length($host) . str_with_length($data);}function str_with_length( $data ){ return pack('n', strlen($data)).$data;}$wk = new WavesKit("W");$address = '3PCAB4sHXgvtu5NPoen6EXR5yaNbvsEA8Fj';$pub_key = '2M25DqL2W4rGFLCFadgATboS8EPqyWAN3DjH12AH5Kdr';$signature = '2w7QKSkxKEUwCVhx2VGrt5YiYVtAdoBZ8KQcxuNjGfN6n4fi1bn7PfPTnmdygZ6d87WhSXF1B9hW2pSmP7HucVbh';$data_string = '0123456789abc';$host_string = 'example.com';$message_bytes = signed_data($host_string, $data_string);$wk->log('i', 'Address: '. $address);$wk->log('i', 'Public key:' . $pub_key);$wk->log('i', 'Signed Data: ' . $message_bytes);$wk->log('i', 'Real signature: '. $signature);$wk->setPublicKey( $pub_key );$is_address_verified = $address === $wk->getAddress();if ( $is_address_verified === true) $wk->log('s', "Address: Verified: TRUE");else $wk->log('e', "Address: Verified: FALSE");$signature_verified = $wk->verify($wk->base58Decode($signature), $message_bytes);if ( $signature_verified === true) $wk->log('s', "Signature Verified: TRUE");else $wk->log('e', "Signature Verified: FALSE");$fake_signature = '29qWReHU9RXrQdQyXVXVciZarWXu7DXwekyV1zPivkrAzf4VSHb2Aq2FCKgRkKSozHFknKeq99dQaSmkhUDtZWsw';$wk->log('i', 'Fake Signature: '. $fake_signature);$signature_verified = $wk->verify($wk->base58Decode($fake_signature), $message_bytes);if ( $signature_verified === true) $wk->log('e', "Fake Signature Verified: TRUE");else $wk->log('s', "Fake Signature Verified: FALSE");?>signTransactionA method for signing transactions in Waves network. See the description of supported transaction types in Transactions section below.Example: { //data – a line ready for sending to Waves network's node (server) }) .catch(error => { //Processing errors });">const txData = { type: 4, data: { amount: { assetId: 'WAVES', tokens: '1.567', }, fee: {

Fake Data - The form filler

Start the file recovery command, you just need to wait patiently.Do Not Write Any New Data to the Drive : New data being written to a fake flash drive jeopardizes the integrity of any critical files that may still be intact on the drive. Ensure the use of the drive is strictly limited.Document and Report : Keep a record of the fake drives you encounter and report these cases to relevant authorities or platforms.Communicate with the Customer : If you are an IT professional, clearly explain the situation to your customer. Let them know that data recovery from a fake flash drive may not be possible.Attempt Recovery : In cases where data has been stored on a counterfeit drive, a professional data recovery service may be able to attempt recovering data. Conclusion Fake flash drives pose a significant risk to data integrity and can lead to data loss. By understanding how to detect fake flash drives and using tools like Validrive and CapacityTester, you can verify the authenticity of your storage devices. Always purchase from reputable sellers and be cautious of deals that seem too good to be true.Renee Undeleter - Powerful Data Recovery SoftwareEasy to use Only simple steps to recover data from storage devices.Multiple scan modes Fast partition scan, whole partition scan and whole disk scan for different recovery needs.File types Support to recover pictures, videos, audios, documents, mails, etc.Supported storage devices Recover data from recycle bin, SD card, external disk, etc.Supported systems Windows 10, 8.1, 8, 7, Vista, XP, 2000 and Mac OS X10.6, 10.7, 10.8.Easy to use Only simple steps to recover data from storage devices.Multiple scan modes - 3 scan modes for different recovery needs.Supported storage devices Recover data from recycle bin, SD card, external disk, etc.. Another way to say Fake Data? Synonyms for Fake Data (other words and phrases for Fake Data). Fake data regeneration is really annoying for project regeneration. I am testing a blueprint and regenerating the project all the time. Would be really nice an option to don't regenerate the fake data. A version of skip-fake-data with skip-fake-data-regeneration option:

Fake Data Generator - sqlable.com

DriveSavers BlogHome » DriveSavers Blog » Identifying Fake Flash Drives: Data Recovery Challenges and Solutions May 22, 2024 DriveSavers Blog USB drives have become essential tools for data storage and transfer. However, the market is flooded with fake flash drives that often cause significant data loss. This blog post aims to help identify these fraudulent devices, understand their impact on data recovery, and ensure data integrity. What are Fake Flash Drives? Fake flash drives deceive buyers into thinking they are purchasing high-capacity USB storage devices. These drives often claim to offer terabytes of storage for prices as low as $9.99. However, their actual usable storage capacity is far less, and they use technical manipulation to misrepresent their capabilities. Unrealistic Storage Capacity and Low Price At this moment in time, genuine flash drives with large storage capacities, such as 1TB or 2TB, generally cost over $80. Flash drives claiming to offer terabytes of storage for a fraction of the usual cost are likely fake or very low quality.The cost of flash memory stays mostly consistent between reputable fabricators, and genuine high-capacity drives reflect this in their pricing. If a deal seems too good to be true, it probably is. Physical Examination Inspect the physical features of the drive. Fake flash drives often use generic housings, or are built to look similar to legitimate brands.Look for inconsistencies in labeling, build quality, and specifications. For example, a drive claiming to be USB 2.0 but advertising fast multi-terabyte storage is highly suspicious because USB 2.0 is limited to the maximum theoretical transfer speed to 480 Mbps. This speed is not well-suited for handling large amounts of data efficiently, which would be a typical use for multi-terabyte storage devices. Also, check for signs of tampering, such as missing or non-genuine parts, and compare the drive’s exterior materials and finish to known genuine models. Suspicious Listings Listings for fake flash drives often contain poor grammar, vague descriptions, and exaggerated claims.Descriptions like “Large-Capacity Storage: USB Flash drive super space” and promises of “excellent water resistance, magnetic resistance, high-temperature resistance, and X-ray resistance” should raise red flags. These listings might also boast features that sound too good to be true, such as being indestructible or having an unlimited warranty. Case Study: Analyzing a Fake Flash Drive DriveSavers has a designated department where we specialize in flash memory data recovery. Here is a detailed examination the team conducted of a fraudulent “2,000GB” flash drive and what we found: Listing Claims The drive was advertised as a 2,000GB USB flash drive with features such as “Plug and Work” and “excellent resistance to water, magnetic fields, high temperatures, and X-rays.” Such exaggerated claims are common in fake listings and are generally irrelevant to the technical functions of the storage device. Behavior Under Test After writing 58GB of data, the drive stopped storing new data correctly. Instead, new files were added to the File Allocation Table at the beginning of the drive but were not actually written in full to the storage