Immune to hacks: Inoculating deep neural networks to thwart attacks
The adaptive immune system serves as a template for defending neural nets
from confusion-sowing attacks

Date:
March 24, 2022
Source:
University of Michigan
Summary:
If a sticker on a banana can make it show up as a toaster,
how might strategic vandalism warp how an autonomous vehicle
perceives a stop sign? Now, an immune-inspired defense system for
neural networks can ward off such attacks, designed by engineers,
biologists and mathematicians.



FULL STORY ==========================================================================
If a sticker on a banana can make it show up as a toaster, how might
strategic vandalism warp how an autonomous vehicle perceives a stop
sign? Now, an immune- inspired defense system for neural networks can ward
off such attacks, designed by engineers, biologists and mathematicians
at the University of Michigan.


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Deep neural networks are a subset of machine learning algorithms used
for a wide variety of classification problems. These include image identification and machine vision (used by autonomous vehicles and other robots), natural language processing, language translation and fraud
detection. However, it is possible for a nefarious person or group to
adjust the input slightly and send the algorithm down the wrong train
of thought, so to speak. To protect algorithms against such attacks,
the Michigan team developed the Robust Adversarial Immune-inspired
Learning System.

"RAILS represents the very first approach to adversarial learning that
is modeled after the adaptive immune system, which operates differently
than the innate immune system," said Alfred Hero, the John H. Holland Distinguished University Professor, who co-led the work published
inIEEE Access.

While the innate immune system mounts a general attack on pathogens,
the mammalian immune system can generate new cells designed to defend
against specific pathogens. It turns out that deep neural networks,
already inspired by the brain's system of information processing, can
take advantage of this biological process, too.

"The immune system is built for surprises," said Indika Rajapakse,
associate professor of computational medicine and bioinformatics and
co-leader of the study. "It has an amazing design and will always find
a solution." RAILS works by mimicking the natural defenses of the immune system to identify and ultimately take care of suspicious inputs to the
neural network. To begin developing it, the biological team studied
how the adaptive immune systems of mice responded to an antigen. The
experiment used the tissues of genetically modified mice that express fluorescent markers on their B cells.



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The team created a model of the immune system by culturing cells from the spleen together with those of bone marrow, representing a headquarters and garrison of the immune system. This system enabled the biological team
to track the development of B cells, which starts as a trial-and-error
approach to designing a receptor that binds to the antigen. Once the
B-cells converge on a solution, they produce both plasma B cells for
capturing any antigens present and memory B cells in preparation for
the next attack.

Stephen Lindsly, a doctoral student in bioinformatics at the time,
performed data analysis on the information generated in Rajapakse's lab
and acted as a translator between the biologists and engineers. Hero's
team then modeled that biological process on computers, blending
biological mechanisms into the code.

They tested the RAILS defenses with adversarial inputs. Then they compared
the learning curve of the B cells learning to attack antigens with the algorithm learning to exclude those bad inputs.

"We weren't sure that we had really captured the biological process
until we compared the learning curves of RAILS to those extracted from
the experiments," Hero said. "They were exactly the same." Not only
was it an effective biomimic, RAILS outperformed two of the most common
machine learning processes used to combat adversarial attacks: Robust
Deep k-Nearest Neighbor and convolutional neural networks.

"One very promising part of this work is that our general framework can
defend against different types of attacks," said Ren Wang, a research
fellow in electrical and computer engineering, who was primarily
responsible for the development and implementation of the software.



==========================================================================
The researchers used image identification as the test case, evaluating
RAILS against eight types of adversarial attacks in several datasets. It
showed improvement in all cases, including protection against the most
damaging type of adversarial attack -- known as a Projected Gradient
Descent attack. In addition, RAILS improved the overall accuracy. For
instance, it helped correctly identify an image of a chicken and an
ostrich, widely perceived as a cat and a horse, as two birds.

"This is an amazing example of using mathematics to understand this
beautiful dynamical system," Rajapakse said. "We may be able to take
what we learned from RAILS and help reprogram the immune system to work
more quickly." Future efforts from Hero's team will focus on reducing
the response time from milliseconds to microseconds.

Hero is also the R. Jamison and Betty Williams Professor of Engineering
and a professor of electrical engineering and computer science, biomedical engineering and statistics. Rajapakse is also an associate professor of mathematics and of biomedical engineering. Lindsly is now at MathWorks.

The project was funded by the Department of Defense, Defense Advanced
Research Projects Agency and the Army Research Office.


========================================================================== Story Source: Materials provided by University_of_Michigan. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Ren Wang, Tianqi Chen, Stephen M. Lindsly, Cooper M. Stansbury,
Alnawaz
Rehemtulla, Indika Rajapakse, Alfred O. Hero. RAILS: A Robust
Adversarial Immune-Inspired Learning System. IEEE Access, 2022;
10: 22061 DOI: 10.1109/ACCESS.2022.3153036 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220324104547.htm

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