⸿ooper Applied Technology.com   (TM SM R)

 We create Pseudocode for Algorithm Instructions & Rules

Algorithms…are the basis of

Artificial Intelligence (AI)---Computer Systems Machine Learning (ML) & subsets Generative Artificial Intelligence (GenAL), Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNN), Natural Language Processing (NLP), computer programming, electronic devices, robotics,

Generative Artificial Intelligence (GenAI) DeepFakes*,

Face Swapping detection learned with Adobe Photoshop in 1987 & then Photoshop Liquidity but before learned Convolutional Neural Networks (CNN)*

*How Can you tell if it’s REAL or FAKE? Ask us.

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emailto: richardcooper@frontier.com  or 5852561449 us, we are real people

How do humans learn? education, experience & practice.

   How do computer systems learn? Algorithm Instructions & Rules,

  training data, data mining & practice.

Pseudocode created for Algorithm Instructions & Rules.

Algorithms are the basis to plan out the logic & validation needed for

    Artificial Intelligence (AI)--Machine Learning (ML) & AI subsets Generative

   Artificial Intelligence (GenAL), Generative Adversarial Networks (GANs),

 Convolutional Neural Networks (CNN), Natural Language Processing (NLP),

 computer programming, electronic devices, robotics.

Artificial Intelligence (AI) & Machine Learning (ML)

To create artificial intelligence (AI) you need to identify the problem you’re trying to

solve, collect the right data, create algorithms, train the AI model, choose the right

 platform, pick a programming language & finally, deploy & monitor the operation.

 Artificial intelligence (AI) technology gives computer systems the ability to mimic

 cognitive functions associated with human intelligence, such as being able

 to see, understand & respond to spoken or written language,

 to analyze data & make recommendations. 

 Machine learning (ML) a sub-set of artificial intelligence (AI) focuses on using

 data & algorithms & be able to imitate the way that humans learn, gradually

 improving its accuracy & ability to learn over time & make informed decisions. 

Generative AI (GenAI) a sub-set of Artificial Intelligence (AI) can generate new content

  including text, images, videos, sounds, animation, 3D models, conversations,

 music, summarizations, Q&A, classifications & other types of data.

GenAI technologies can mimic human intelligence in nontraditional computing tasks

 like image recognition, the ability to interpret, manipulate & comprehend

  human language or any complex subject matter from new data & reuse training

 data to solve new problems.

GenAI is trained to recognize patterns & structures in data & enable users to quickly

  generate new content based on a variety of inputs using neural networks

 both unsupervised & semi-supervised learning or training.

 For example, GenAL can learn English vocabulary & create a poem from the words

 it processes, respond to chatbots, media creation, product development & design

 & additional needs your mind can conceive.

 Misinformation & Deception---D Day 1944

A crucial part of their preparations for D-Day 6 June 1944, the Allies developed

 a deception plan to draw attention away from the Normandy Invasion,

 code named Operation 'Fortitude' & part of a larger overall deception strategy

 Operation 'Bodyguard'.

The PLAN: misinformation & deception & fake radio traffic and decoy equipment

including inflatable tanks and dummy landing craft mimicked preparations for

 a large-scale invasion for the Pas de Calais.

 Lieutenant M.E. Clifton James was employed, an Australian actor who bore

  a striking resemblance to Bernard Montgomery to impersonate the British general.

  James donned one of the general’s uniforms & black berets & flew to Gibraltar on

 May 26, 1944 & then to Algiers where German intelligence was sure to spot him.

 German Intelligence surmised that no attack across the English Channel could be

 imminent with the Allied general scouting the Mediterranean.

Double agents delivered misinformation to the enemy to reinforce this deceit

 before the Normandy landings.

 This misinformation, deception & fake scheme saved thousands of allied lives.

DeepFakes Generative Artificial Intelligence (GenAI)

DeepFakes are created with generative artificial intelligence (GenAI) for misinformation

 & deception can be an image, video, audio, voice or text.

  The “deep” is “deep learning,” a method of training computer systems on massive

  amounts of data to perform human-like tasks.

 The “fake” indicates that the media is computer-generated by algorithm instructions

 & rules for generative artificial intelligence (GenAI) & generative

 adversarial networks (GANs) to make fakes difficult to distinguish

 from human-generated media.

Creating fake content manually has been done over many years but now by AI.

 Generative Adversarial Networks (GANs) are neural learning networks specifically

 designed to imitate the structure & function of the human brain. GANs use

two trained neural networks to compete against each other to generate

    fake media from a given training dataset. One multilevel neural network is

  for generating fake images from existing databases & the second multilevel

 neural network for verifying generated images look real deepfakes.

    DeepFakes pose a considerable threat to the authenticity of both online & offline

 content & is difficult to differentiate fake content from authentic content.

  DeepFakes objective is to fool the media viewer or a technology system to create

 discord, threaten an organization’s brand, impersonate leaders, enable access to

 networks & sensitive information just to mention a few nefarious objectives.

How can you tell if It Real or Fake?  Detect & Expose DeepFakes

DeepFakes uses artificial intelligence (AI) that created it & can be used to detect 

  deepfakes. Deepfakes detection can prevent the spread of fake content by

  analyzing & comparing the content to inclusive training datasets & large

  amounts of reference data, for ways to detect & expose the media if it

 has been digitally manipulated into synthetic media.

Hints to spot a deepfakes

We use several of the following manual deepfake detection & exposure methods

 in addition to algorithms, generative artificial intelligence (GenAI), generative

 adversarial networks (GANs) to identify unnatural patterns that signify that

the content has been modified or artificially created.

Examples:

Facial and body movement

Facial Biometrics

Lip-sync detection

Inconsistent -- or lack of -- eye blinking

Irregular reflections or shadowing

Pupil dilation

Artificial audio noise

 More Examples:

Visual Artifacts Analysis

Audio-Visual Synchronization

Texture Analysis

Contextual Analysis

Deep Learning Models

Convolutional Neural Network (CNN)*

*Convolutional Neural Network (CNN) is a machine learning (ML) algorithm used for

 image recognition & processing typically to blur & sharpen images & perform

 manipulative operations.

 Prior to CNN,  Adobe Photoshop developed Photoshop Liquidity tool to detect

 whether faces in photos were manipulated.

Face Swapping. How can you tell if it’s real or fake?

Face Swapping detection & exposure we learned with Adobe Photoshop in 1987

   & then Photoshop Liquidity but before Convolutional Neural Networks (CNN) of today.

 

ABOUT US

Cooper Applied Technology (USA, family business) has transformed from an outdated

 inefficient analog company into a development to deployment technology team to

 write pseudocode for algorithms instructions & rules that are the basis

 to plan logic & validation for artificial intelligence (AI) and subsets.

 Deepfakes Techniques Detection & Exposure.

Face Swapping detection & exposure we learned with Adobe Photoshop in 1987

& then Photoshop Liquidity but before we learned Convolutional Neural Networks (CNN).

 I acquired my practical & business technology knowledge & experience

 as a Director, CEO, CTO, VP, employee of various companies before joining

  Cooper Applied Technology  (SM TM R)

I earned BS degrees in Accounting & Electrical Engineering at RIT,

  MBA degrees in Finance & Economics at Syracuse University.

Richard Cooper CEO  CTO

 Cooper Applied Technology updates our web page as Technology continues to update.

 We are Real People here not AI ChatGPG

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Rochester, NY 14618  USA   V: 585-256-1449

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