These Hoodies and Sweatshirts Can Fool Surveillance Algorithms

The state of the art AI-driven surveillance technology has given spying powers to every camera. We think of surveillance cameras as highly advanced digital eyes, watching over us, or watching out for us. With the help of AI, these cameras now have brains to complement their eyes. While this is good news for public safety, helping police forces and detectives more easily spot crimes and accidents and have a range of scientific and industrial applications, conversely its invasion of privacy. So the question is —

Is there a way we can trick these surveillance algorithms and become “invisible”?

Researchers from Facebook and the University of Maryland have an answer. Nicknamed as — “invisibility cloaks” for A.I. — where the researchers from Facebook and University of Maryland — have made a series of sweatshirts and T-shirts that can trick surveillance algorithms that renders the wearer imperceptible to detectors. The research work presents a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, they trained patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Researchers printed adversarial examples on sweatshirts and other items to “attack” object detectors and cause them to fail to recognize their targets from images or videos.

To go in the details — the Facebook and Maryland research team ran 10,000 images of people through a detection algorithm to create the deceptive t-shirts. When a person was detected, they were replaced with randomized changes of attributes like contrast, brightness, etc. To verify the effectiveness, a series of algorithms were then used to find if the randomized patterns can trick the algorithms or not. Once done, these patterns were printed on physical objects like dolls, papers, clothing — hoodies, and sweatshirts. When a person wears these sweatshirts, the detector’s ability to identify them falls down to 50%.

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The pullover is a great way to stay warm this winter, whether in the office or on the roads.

It has a stay-dry microfleece lining, a modern fit, and adversarial patterns the evade most common object detectors. The YOLOv2 detector is evaded using a pattern trained on the COCO dataset with a carefully constructed objective.

#programming #data-science #artificial-intelligence #algorithms

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These Hoodies and Sweatshirts Can Fool Surveillance Algorithms

These Hoodies Make You ‘Invisible’ to Some Surveillance Algorithms

They were created by researchers from Facebook and the University of Maryland

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The spread of artificial intelligence into surveillance technology has given every CCTV camera the potential to turn into a spy for the state. And on the internet, images scraped from social media sites or videos can be used to build massive surveillance databases like Clearview AI.

A hoodie might change that.

Researchers from Facebook and the University of Maryland have made a series of sweatshirts and T-shirts that trick surveillance algorithms into not detecting the wearer. They’ve dubbed them “invisibility cloaks” for A.I.

The shirts exploit a quirk that was found in computer vision algorithms nearly five years ago. These algorithms use a simple, even naive approach to identifying objects: They look for patterns of pixels in a new image that resemble patterns they’ve seen before. Humans can use complex clues or real-world knowledge when they’re looking at something new, but algorithms just use pattern matching.

That means if you know the pattern the algorithm is looking for, you can hide it. In order to create the algorithm-fooling shirts, the Facebook and Maryland team ran 10,000 images of people through a detection algorithm. When a person was detected, they were replaced with randomized changes of perspective, brightness, and contrast. Another algorithm was then used to find which of the randomized changes was most effective at tricking the algorithm.

When those randomized patterns were printed on physical objects, like posters, paper dolls, and finally clothing, the detection algorithms were still tricked. Researchers noted however that the accuracy of these real-world tests was lower compared to the purely digital tests. When a person is wearing the sweatshirt, the detector’s ability to recognize them goes from nearly 100% to 50%, the likelihood of a coin toss.

This research continues a line of work being done by the University of Maryland computer science department, some of whom joined Facebook in 2018 and 2019. Previously, the lab researched how these same principles of tricking A.I. could be used to fool copyright detection algorithms, like the ones used by YouTube to prevent unauthorized use of copyrighted music, in order to call attention to how easy they were to evade.

The Tools to Defeat Facial Recognition Are Free Online

It only takes two stickers to fool this popular face detector

onezero.medium.com

The work could benefit Facebook, too. The attack fundamentally works because image recognition algorithms lack any context or understanding of the images they analyze. Figuring out how they fail is a first step to make algorithms that don’t fall for these kinds of tricks. It’s the beginning of a research process that would not only make algorithms more resistant to attack but, in theory, greatly boost their accuracy and flexibility since their view of the images is less simplistic. In other words, the research could be a way of bolstering the strength of image-detection algorithms rather than destroying them.

You can actually buy a T-shirt or sweatshirt printed with the algorithm-fooling design, but right now, it wouldn’t likely protect your identity from surveillance technology. The researchers tested the designs on popular open-source algorithms and not the proprietary algorithms built by surveillance firms like NEC.

The design also is meant to evade person detection, not facial recognition, which specifically targets aspects of a person’s face rather than their entire body. Person detection is used in public spaces for tasks like counting crowds or seeing if a person is approaching a smart doorbell and, in some cases, to augment facial recognition. But the research, and its turn into reproducible fashion, represents a shifting landscape in surveillance technology in which people can subvert a state-of-the-art algorithm with a simple piece of clothing and then manufacture the design for anyone who wants it.

And even if it doesn’t work, a sweatshirt plastered with an A.I.-generated surveillance spoofer is a great conversation piece.

#artificial-intelligence #surveillance #algorithms #privacy #tech #algorithms

These Hoodies and Sweatshirts Can Fool Surveillance Algorithms

The state of the art AI-driven surveillance technology has given spying powers to every camera. We think of surveillance cameras as highly advanced digital eyes, watching over us, or watching out for us. With the help of AI, these cameras now have brains to complement their eyes. While this is good news for public safety, helping police forces and detectives more easily spot crimes and accidents and have a range of scientific and industrial applications, conversely its invasion of privacy. So the question is —

Is there a way we can trick these surveillance algorithms and become “invisible”?

Researchers from Facebook and the University of Maryland have an answer. Nicknamed as — “invisibility cloaks” for A.I. — where the researchers from Facebook and University of Maryland — have made a series of sweatshirts and T-shirts that can trick surveillance algorithms that renders the wearer imperceptible to detectors. The research work presents a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, they trained patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Researchers printed adversarial examples on sweatshirts and other items to “attack” object detectors and cause them to fail to recognize their targets from images or videos.

To go in the details — the Facebook and Maryland research team ran 10,000 images of people through a detection algorithm to create the deceptive t-shirts. When a person was detected, they were replaced with randomized changes of attributes like contrast, brightness, etc. To verify the effectiveness, a series of algorithms were then used to find if the randomized patterns can trick the algorithms or not. Once done, these patterns were printed on physical objects like dolls, papers, clothing — hoodies, and sweatshirts. When a person wears these sweatshirts, the detector’s ability to identify them falls down to 50%.

Image for post

The pullover is a great way to stay warm this winter, whether in the office or on the roads.

It has a stay-dry microfleece lining, a modern fit, and adversarial patterns the evade most common object detectors. The YOLOv2 detector is evaded using a pattern trained on the COCO dataset with a carefully constructed objective.

#programming #data-science #artificial-intelligence #algorithms

Siphiwe  Nair

Siphiwe Nair

1624027140

Are there Biases in Big Data Algorithms. What can we do?

Big Data and Machine Learning appear to be the advanced buzzword answers for each issue. Sectors, for example, fraud prevention, healthcare, and sales are only a couple of the places that are thought to profit by self-learning and improving machines that can be trained on colossal datasets.

Notwithstanding, how cautiously do we examine these algorithms and research potential biases that could affect results?

Companies utilize different sorts of big data analytics to make decisions, correlations, and anticipate about their constituents or partners. The market for data is huge and developing quickly; it’s assessed to hit $100 billion before the decade’s end.

Data and data sets are not unbiased; they are manifestations of human design. We give numbers their voice, draw insights from them, and define their significance through our understandings. Hidden biases in both the analysis stages present extensive risks, and are as essential to the big-data equation as the numbers themselves.

While such complex datasets may contain important data on why customers decide to purchase certain items and not others, the scale and size of the available information makes it unworkable for an individual to analyse it and recognize any patterns present.

This is the reason machine learning is frequently regarded as the solution to the ‘Big Data Problem.’ Automation of the analysis is one way to deal with deconstructing such datasets, however regular algorithms should be pre-programmed to think about specific factors and search for specific levels of significance.

Algorithms of this sort have existed for quite a long time and a lot of the time are utilized by companies to have the option to scale their tasks, by utilizing repeatable patterns that can be applied to everybody.

This implies that, regardless of whether you’re keen on big data, algorithms, and tech, or not, you’re a part of this today, and it will influence you to an ever-increasing extent.

#big data #latest news #biases in big data algorithms #are there biases in big data algorithms. what can we do? #algorithms #web

A greedy algorithm is a simple

The Greedy Method is an approach for solving certain types of optimization problems. The greedy algorithm chooses the optimum result at each stage. While this works the majority of the times, there are numerous examples where the greedy approach is not the correct approach. For example, let’s say that you’re taking the greedy algorithm approach to earning money at a certain point in your life. You graduate high school and have two options:

#computer-science #algorithms #developer #programming #greedy-algorithms #algorithms

Tia  Gottlieb

Tia Gottlieb

1596427800

KMP — Pattern Matching Algorithm

Finding a certain piece of text inside a document represents an important feature nowadays. This is widely used in many practical things that we regularly do in our everyday lives, such as searching for something on Google or even plagiarism. In small texts, the algorithm used for pattern matching doesn’t require a certain complexity to behave well. However, big processes like searching the word ‘cake’ in a 300 pages book can take a lot of time if a naive algorithm is used.

The naive algorithm

Before, talking about KMP, we should analyze the inefficient approach for finding a sequence of characters into a text. This algorithm slides over the text one by one to check for a match. The complexity provided by this solution is O (m * (n — m + 1)), where m is the length of the pattern and n the length of the text.

Find all the occurrences of string pat in string txt (naive algorithm).

#include <iostream>
	#include <string>
	#include <algorithm>
	using namespace std;

	string pat = "ABA"; // the pattern
	string txt = "CABBCABABAB"; // the text in which we are searching

	bool checkForPattern(int index, int patLength) {
	    int i;
	    // checks if characters from pat are different from those in txt
	    for(i = 0; i < patLength; i++) {
	        if(txt[index + i] != pat[i]) {
	            return false;
	        }
	    }
	    return true;
	}

	void findPattern() {
	    int patternLength = pat.size();
	    int textLength = txt.size();

	    for(int i = 0; i <= textLength - patternLength; i++) {
	        // check for every index if there is a match
	        if(checkForPattern(i,patternLength)) {
	            cout << "Pattern at index " << i << "\n";
	        }
	    }

	}

	int main() 
	{
	    findPattern();
	    return 0;
	}
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main6.cpp hosted with ❤ by GitHub

KMP approach

This algorithm is based on a degenerating property that uses the fact that our pattern has some sub-patterns appearing more than once. This approach is significantly improving our complexity to linear time. The idea is when we find a mismatch, we already know some of the characters in the next searching window. This way we save time by skip matching the characters that we already know will surely match. To know when to skip, we need to pre-process an auxiliary array prePos in our pattern. prePos will hold integer values that will tell us the count of characters to be jumped. This supporting array can be described as the longest proper prefix that is also a suffix.

#programming #data-science #coding #kmp-algorithm #algorithms #algorithms