Adversarial Input Detection Using Image Processing Techniques (IPT)
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Modern deep learning models for the computer vision domain are vulnerable against adversarial attacks. Image prepossessing technique based defense against malicious input is currently considered obsolete as this defense is not effective against all types of attacks. The advanced adaptive attack can easily defeat pre-processing based defenses. In this paper, we proposed a framework that will generate a set of image processing sequences (several image processing techniques in a series). We randomly select a set of Image processing technique sequences (IPTS) dynamically to answer the obscurity question in testing time. This paper outlines methodology utilizing varied datasets examined with various adversarial data manipulations. For specific attack types and dataset, it produces unique IPTS. The outcome of our empirical experiments shows that the method can efficiently employ as processing for any machine learning models. The research also showed that our process works against adaptive attacks as we are using a non-deterministic set of IPTS for each adversarial input.
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Review and discussion of the paper 'Adversarial Attacks and Defenses in Deep Learning' by Kui Ren, Tiahnhang Zheng, Zhan Qin, Xue Liu (2020) for Machine Learning Book Club.
Robustness in deep learning
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
Se research update
1) The document summarizes a research update presentation on software engineering and artificial intelligence given by Assistant Professor Nacha Chondamrongkul.
2) It discusses how software engineering research tackles different stages of software production to minimize costs, efforts, and failures. It also examines how AI can be applied to enhance software engineering processes and how software engineering principles are needed to develop AI systems.
3) Key challenges discussed include how to specify requirements for intelligent systems, test AI systems given their unpredictability, and address issues around reliability, fairness, and deployment when integrating machine learning models into complex software.
Similar to Adversarial Input Detection Using Image Processing Techniques (IPT) (9)
AI Cybersecurity: Pros & Cons. AI is reshaping cybersecurity
AI Cybersecurity: Pros & Cons. AI is reshaping cybersecurity
slides_security_and_privacy_in_machine_learning.pptx
slides_security_and_privacy_in_machine_learning.pptx
Adversarial Attacks and Defenses in Deep Learning.pdf
Adversarial Attacks and Defenses in Deep Learning.pdf
More from Kishor Datta Gupta
GAN introduction.pptx
Generative adversarial networks (GANs) are a class of machine learning frameworks where two neural networks contest with each other in a game. One network generates new data instances, while the other evaluates them for authenticity. The generator creates synthetic instances to fool the discriminator, while the discriminator learns to identify the generator's fakes from true instances.
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Detecting brown spot in rice leaf is an urgent complication in the agricultural field as Brown Spot disease lessen the rice yield remarkably. Several segmentation techniques have been applied to identify and extract the infected portion of the rice-leaf and machine learning algorithms such as decision trees, support vector machines are applied to detect this infection. In particular, a combination of Convolution Neural Networks with these algorithms has also tried to resolve this problem. Although this attempt has achieved success in providing accuracy (96.8%), these kinds of approaches raise issues regarding the size and interpretability of feature space and interpretability of the decision model. Indeed, Deep learning networks automatically create a feature space that usually contains a massive number of features (numerous of them are not necessarily appropriate). This vast number of features extends the non-interpretability of the machine learning model. Furthermore, training the model with these many features is computationally expensive. To resolve these issues, we propose a method to extract a few interpretable features from rice-leaf images and construct a low-dimensional feature space; however, interpretation shows that they deserve significant credit for the decent accuracy of our classification model.
A safer approach to build recommendation systems on unidentifiable data
Conference: 14th International Conference on Agents and Artificial Intelligence (ICAART 2022)
In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, "encryption". Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user's movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.
Adversarial Attacks and Defense
This document summarizes a presentation on machine learning models, adversarial attacks, and defense strategies. It discusses adversarial attacks on machine learning systems, including GAN-based attacks. It then covers various defense strategies against adversarial attacks, such as filter-based adaptive defenses and outlier-based defenses. The presentation also addresses issues around bias in AI systems and the need for explainable and accountable AI.
Zero shot learning
Zero-shot learning allows a model to recognize classes that it was not trained on by utilizing auxiliary information about both seen and unseen classes during training. The model is trained to predict this auxiliary information, like word embeddings or manually designed features, for the seen classes. During testing, the model predicts the auxiliary information for an unseen class and assigns it to the class whose auxiliary information is closest, even if that class was not part of the training data. This allows the model to generalize to new classes without requiring labeled examples of those classes.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
Machine learning in computer security
Machine learning can be applied in various areas of computer security like network security, endpoint protection, application security, user behavior analysis, and process behavior analysis. Some common machine learning techniques that are useful for security include regression for prediction and detection of anomalies, classification to identify threats and attacks, and clustering for forensic analysis and to detect outliers. Example applications of machine learning in security include using regression to detect anomalies in network traffic, classification to identify malware, and clustering to separate malware from legitimate files.
Policy Based reinforcement Learning for time series Anomaly detection
This document discusses a policy-based reinforcement learning approach called PTAD for time series anomaly detection. PTAD formulates anomaly detection as a Markov Decision Process and uses an asynchronous actor-critic algorithm to learn a stochastic policy. The agent takes as input current and previous time series data and actions, and outputs a decision of normal or anomalous. It is rewarded based on a confusion matrix calculation. Experimental results show PTAD achieves best performance both within and across datasets by adjusting to different behaviors. The stochastic policy allows exploring precision-recall tradeoffs. While interesting, it is not compared to neural network based techniques like autoencoders.
Cyber intrusion
This document discusses intrusion detection systems (IDS). It covers the key components of an IDS, including methods of intrusion detection like audit trail processing, on-the-fly processing, profiles of normal behavior, signatures of abnormal behavior, and parameter pattern matching. The document also discusses building network-based IDS using tools like Snort and host-based IDS. It provides examples of labs to analyze network and wireless intrusion detection using machine learning techniques.
understanding the pandemic through mining covid news using natural language p...
This document summarizes a research presentation on analyzing Covid-19 news reports from newspapers in developed and developing countries using natural language processing. It introduces the research aim to understand how newspapers portray the pandemic using NLP techniques on reports from the US and Bangladesh. The researchers collected over 1000 news articles to create the NNK Dataset, which they preprocessed and analyzed to extract keywords, sentiments, and case numbers. Word clouds of frequent terms and numeric extractions showed how coverage evolved over time. The dataset was made publicly available to encourage further analysis of portraying pandemics through newspapers.
Different representation space for MNIST digit
The document discusses using different representation spaces for digits when classifying MNIST data. It shows that classifying digits when each digit has its own best representation space that other digits are compared to leads to much higher accuracy, ranging from 97-99%, compared to using the same representation space for all digits which only achieves 64% accuracy.
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. However, total dependency on machine-based prediction is yet a far fetched concept. In this paper, we provide a framework utilizing several Natural Language Processing (NLP) algorithms to construct a comparative analysis. We create an ensemble of top-performing algorithms to accomplish classification task on medical reports. We compare both the traditional machine learning and deep learning techniques and evaluate their probabilities of being reliable on analyzing medical diagnosis. We concluded that an ensemble approach can provide reliable outcomes with accuracy over 92% and that the current state of the art is unequipped to provide the result with the standard needed for health sectors but an ensemble of these techniques can be a pathway for future research direction.
Conference: IEEE 11th Annual Information Technology, Electronics and Mobile Communication Conference (IEEE IEMCON 2020)At: Vancouver
Clustering report
This document discusses clustering clean and adversarial images from the MNIST dataset using K-means, LDA, and T-SNE clustering methods. It contains 10,000 clean images and 10,000 adversarial images generated using the FGSM attack method from 10 classes in MNIST. The document applies principal component analysis to extract features from the images before clustering them to visualize how the different methods group the clean and adversarial samples.
Basic digital image concept
This document discusses basic digital image concepts including image data representations, color channels, bit depth, CMYK vs RGB color models, image blur filters using kernels, and kernel operations used in convolutional neural networks (CNN).
An empirical study on algorithmic bias (aiml compsac2020)
In all goal-oriented selection activities, an existence of certain level of bias is unavoidable and may be desired for efficient artificial intelligence based decision support systems. However, a fair independent comparison of all eligible entities is essential to alleviate explicit bias in competitive marketplace. For example, searching online for a good or service, it is expected that the underlying algorithm will provide fair results by searching all available entities in the category mentioned. However, a biased search can make a narrow or collaborative query, ignoring competitive outcomes, resulting customers in costing more or getting lower quality products or services for the money they spend. This paper describes algorithmic bias in different contexts with examples and scenarios, best practices to detect bias, and two case studies to identify algorithmic bias.
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
This document proposes a hybrid PoW-PoS consensus protocol to prevent 51% attacks in cryptocurrency systems. The key points are:
1) A 51% attack occurs when a single entity gains over 50% of the network's computational power, allowing them to manipulate the blockchain.
2) Existing solutions have limitations like special validators, inconsistent block times, or reliance on another network.
3) The proposed solution uses PoW and PoS sequentially in each node, adjusting difficulty to maintain consistent block times. It penalizes malicious nodes and distributes profits proportionally.
4) Analysis shows the hybrid protocol maintains decentralization and the blockchain properties better than alternatives like Casper, Decred or Komodo
Shamir secret sharing: Alternative of hashing for authentication
Hashing is vulnerable to quantum attack. So keep password text hidden using hash in quantum computer era is not viable. Shamir's secret sharing can be used for authentication purpose as a alternative of hashing
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
Multi level ransomware analysis MALCON 2019 conference
Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different malware families, but the problem remained unsolved due to the continuously evolving malware. In this work, we proposed a multi-level big data mining framework combining Reverse engineering, Natural Language Processing(NLP) and Machine Learning(ML) approaches. The framework analyzes the ransomware at different levels (i.e., Dynamic link library, function call and assembly instruction level) via different supervised ML algorithms. Apache Spark was employed for faster processing of large generated feature set. Portable Executable (PE) parser and Objectdump tool of Linux system were used to get the raw data from the ransomware and normal binaries that were processed further using our custom-built NLP processing. The n-gram probabilities, term-frequency and inverse document frequency (TF-IDF) were used to generate the final feature sets. Experiments were performed with different ‘N’ values of n-gram language model that shows that the ransomware detection accuracy is inversely proportional to the value of N. Among the five chosen supervised classifiers, Logistic regression outperformed others with a detection rate of 98.59% for generated TF-IDFs trigrams at combined multi-level, which is an improved accuracy compared to individual levels.
COMXAI A tool to explain AI USING FAULT LOCATION
This document discusses a tool called COMXAI that uses combinatorial testing methods to help explain the decisions made by artificial intelligence and machine learning models. COMXAI is able to narrow down combinations of input factors that are responsible for certain model outcomes, which can then be presented as understandable rules. Some limitations of this approach are that it may generate very large rulesets with many input combinations, and it is vulnerable to overfitting issues in the underlying models. The authors propose future work to address these limitations, as well as improving the user interface and security of the tool.
More from Kishor Datta Gupta (20)
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
A safer approach to build recommendation systems on unidentifiable data
A safer approach to build recommendation systems on unidentifiable data
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Policy Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
understanding the pandemic through mining covid news using natural language p...
understanding the pandemic through mining covid news using natural language p...
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
An empirical study on algorithmic bias (aiml compsac2020)
An empirical study on algorithmic bias (aiml compsac2020)
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Shamir secret sharing: Alternative of hashing for authentication
Shamir secret sharing: Alternative of hashing for authentication
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Multi level ransomware analysis MALCON 2019 conference
Multi level ransomware analysis MALCON 2019 conference
Recently uploaded
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Presentation from Ghazal Aakel and Eric Jochum at the GSD Community Stage Meetup on June 6th, 2024
Measures in SQL (SIGMOD 2024, Santiago, Chile)
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Graspan: A Big Data System for Big Code Analysis
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Energy consumption of Database Management - Florina Jonuzi
Presentation from Florina Jonuzi at the GSD Community Stage Meetup on June 06, 2024
Transform Your Communication with Cloud-Based IVR Solutions
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
What is Master Data Management by PiLog Group
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
GreenCode-A-VSCode-Plugin--Dario-Jurisic
Presentation about a VSCode plugin from Dario Jurisic at the GSD Community Stage meetup
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Hand Rolled Applicative User Validation Code Kata
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
OpenMetadata Community Meeting - 5th June 2024
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
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✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
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UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
Webinar On-Demand: Using Flutter for Embedded
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Recently uploaded (20)
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
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Energy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina Jonuzi
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
Adversarial Input Detection Using Image Processing Techniques (IPT)
- 1. Adversarial Input Detection Using Image Processing Techniques (IPT) Authors: • Kishor Datta Gupta • Dipankar Dasgupta • Zahid Akhtar Presented by : Kishor Datta Gupta
- 2. Adversarial Attack (AA) on AI/ML Types: • Poisoning Attack : Manipulate training data • Evasion Attack: Manipulate input data • Trojan AI : Manipulate AI Architecture (example: Changes weights value) “Manipulation of training data, Machine Learning (ML) model architecture, or manipulate testing data in a way that will result in wrong output from ML”
- 3. Different Adversarial Traits Different Attack method has different types of noise/manipulation style
- 4. Detection of Adversarial Traits(1) Clean and adversarial images have quantifiable noise difference
- 5. Detection of Adversarial Traits(2)
- 6. Detection of Adversarial Traits(3)
- 7. Methodology
- 9. Comparison
- 10. Summary Work against adaptive attack Don’t reduce ML efficiency Can identify attack type Applicable for cross-platform Work for both blackbox and whitebox attack Our defense has below properties:
- 11. Q/A