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Q2322016
Banco de Dados
Texto associado
Atenção
Quando referidas, considere as tabelas relacionais TX e TY, criadas e instanciadas com o script SQL a seguir.
create table TY(C int primary key not null, A int)
create table TX(A int primary key not null, B int,
foreign key (B) references TY(C)
on delete cascade
)
insert into TY values (1,0)
insert into TY(C) values (2)
insert into TY(C) values (3)
insert into TY values (5,NULL)
insert into TY values (6,NULL)
insert into TX values (1,2)
insert into TX values (2,1)
insert into TX values (3,2)
insert into TX values (4,2)
Num hipotético índice do tipo BitMap, sobre a coluna B da tabela TX,
como descrita anteriormente, o conteúdo seria:
Q2322015
Banco de Dados
Texto associado
Atenção
Quando referidas, considere as tabelas relacionais TX e TY, criadas e instanciadas com o script SQL a seguir.
create table TY(C int primary key not null, A int)
create table TX(A int primary key not null, B int,
foreign key (B) references TY(C)
on delete cascade
)
insert into TY values (1,0)
insert into TY(C) values (2)
insert into TY(C) values (3)
insert into TY values (5,NULL)
insert into TY values (6,NULL)
insert into TX values (1,2)
insert into TX values (2,1)
insert into TX values (3,2)
insert into TX values (4,2)
Com referência às tabelas TX e TY, como descritas anteriormente,
analise o comando SQL a seguir.
delete FROM TY
Após a execução desse comando, o total de linhas, somando-se os números de linhas de cada tabela, é:
delete FROM TY
Após a execução desse comando, o total de linhas, somando-se os números de linhas de cada tabela, é:
Q2322014
Banco de Dados
Texto associado
Atenção
Quando referidas, considere as tabelas relacionais TX e TY, criadas e instanciadas com o script SQL a seguir.
create table TY(C int primary key not null, A int)
create table TX(A int primary key not null, B int,
foreign key (B) references TY(C)
on delete cascade
)
insert into TY values (1,0)
insert into TY(C) values (2)
insert into TY(C) values (3)
insert into TY values (5,NULL)
insert into TY values (6,NULL)
insert into TX values (1,2)
insert into TX values (2,1)
insert into TX values (3,2)
insert into TX values (4,2)
Com referência às tabelas TX e TY, como descritas anteriormente, analise o comando SQL a seguir.
insert into TX(A, B)
select C,A FROM TY
where C not in (select A from TX)
or A in (select A from TX)
O conjunto de linhas inseridas é:
Q2322013
Banco de Dados
Texto associado
Atenção
Quando referidas, considere as tabelas relacionais TX e TY, criadas e instanciadas com o script SQL a seguir.
create table TY(C int primary key not null, A int)
create table TX(A int primary key not null, B int,
foreign key (B) references TY(C)
on delete cascade
)
insert into TY values (1,0)
insert into TY(C) values (2)
insert into TY(C) values (3)
insert into TY values (5,NULL)
insert into TY values (6,NULL)
insert into TX values (1,2)
insert into TX values (2,1)
insert into TX values (3,2)
insert into TX values (4,2)
Com referência às tabelas TX e TY, como descritas anteriormente,
analise o comando SQL a seguir.
select count(*) from TX t1 left join TY t2 on t1.B=t2.A
O valor exibido pela execução desse comando é:
select count(*) from TX t1 left join TY t2 on t1.B=t2.A
O valor exibido pela execução desse comando é:
Q2322012
Redes de Computadores
Marcos, analista do TCE SP, foi informado de que o estagiário
Mário não consegue enviar um arquivo para o seu encarregado,
Jair. Após alguns testes, Marcos identificou que:
• As máquinas que estão conectadas no mesmo comutador já se comunicam entre si;
• A máquina do Mário está na Vlan 20, no switch Alfa e porta 8;
• A máquina do Jair se conecta através da porta 10, do switch Bravo e Vlan 20;
• Ambos os switches possuem máquinas na Vlan 20 e Vlan200; e
• A porta para interligação entre os switches é a porta 24 de cada equipamento.
Para resolver o problema, Marcos deve configurar a porta:
• As máquinas que estão conectadas no mesmo comutador já se comunicam entre si;
• A máquina do Mário está na Vlan 20, no switch Alfa e porta 8;
• A máquina do Jair se conecta através da porta 10, do switch Bravo e Vlan 20;
• Ambos os switches possuem máquinas na Vlan 20 e Vlan200; e
• A porta para interligação entre os switches é a porta 24 de cada equipamento.
Para resolver o problema, Marcos deve configurar a porta:
Q2322011
Redes de Computadores
Luiza deseja iniciar uma chamada VoIP com João. Então sua
mensagem SIP foi:
INVITE sip: [email protected] SIP/2.0 Via: SIP/2.0/UDP 167.180.112.24 From: sip:[email protected] To: sip:[email protected] CALL-ID: [email protected] Content-Type: application/sdp Content-Length: 885
c=IN IP4 167.180.112.24 m=audio 38060 RTP/AVP 0
Acerca da mensagem acima, é correto afirmar que:
INVITE sip: [email protected] SIP/2.0 Via: SIP/2.0/UDP 167.180.112.24 From: sip:[email protected] To: sip:[email protected] CALL-ID: [email protected] Content-Type: application/sdp Content-Length: 885
c=IN IP4 167.180.112.24 m=audio 38060 RTP/AVP 0
Acerca da mensagem acima, é correto afirmar que:
Q2322010
Redes de Computadores
O SNMP é usado para transmitir informações e comandos entre
uma entidade gerenciadora e um agente que os executa em
nome da entidade dentro de um dispositivo de rede gerenciado.
Em alguns casos, ocorre uma sobrecarga devido às múltiplas
mensagens que devem ser enviadas.
Para evitar essa sobrecarga, deve ser utilizada a PDU:
Para evitar essa sobrecarga, deve ser utilizada a PDU:
Q2322009
Redes de Computadores
O analista João identificou que a máquina ALFA com IPv6
::ffff:c0a8:acf5 está gerando um tráfego anômalo e necessita
identificar o endereço MAC da máquina ALFA para tomar as
medidas necessárias.
Considerando a utilização do IPv6, João deve utilizar:
Considerando a utilização do IPv6, João deve utilizar:
Q2322008
Redes de Computadores
Um determinado roteador está interligando diversos enlaces,
cada um rodando diferentes protocolos da camada de enlace
com diferentes MTUs. Ao receber um datagrama IPv4 de um
enlace, o roteador identificou que o enlace de saída tem uma
MTU menor do que o comprimento do datagrama IP.
Nesse cenário, o roteador deve:
Nesse cenário, o roteador deve:
Q2322007
Segurança da Informação
Renata presta serviço para a área de redes para a empresa B.
Renata identificou que muitos usuários ainda faziam uso do
telnet para efetuar login remoto e iniciou a implementação do
SSH. Este garante segurança no login remoto, além de oferecer
uma capacidade cliente/servidor mais genérica, e pode ser usado
para funções de rede como transferência de arquivos e e-mail.
Para a implementação correta, o SSH faz uso de vários
protocolos.
De forma a multiplexar o túnel encriptado para diversos canais lógicos existentes, Renata deverá usar o protocolo:
De forma a multiplexar o túnel encriptado para diversos canais lógicos existentes, Renata deverá usar o protocolo:
Q2322006
Segurança da Informação
O Ataque “Ping da morte" é um ataque de negação de serviço
(DoS) em que o invasor visa interromper uma máquina alvo
enviando um pacote maior que o tamanho máximo permitido,
fazendo com que a máquina visada trave ou falhe. De forma a
evitar esse ataque, o Departamento de Segurança do Tribunal de
Contas do Estado de São Paulo (TCE SP) resolveu bloquear as
requisições de PING, impedindo que outro computador utilize
esse comando para obter informações sobre o servidor.
Para isso, efetuará o bloqueio com o Iptables.
Sabendo-se que a interface do servidor é eth0, o comando de aplicação da regra de bloqueio no IPTABLES é:
Sabendo-se que a interface do servidor é eth0, o comando de aplicação da regra de bloqueio no IPTABLES é:
Q2322005
Segurança da Informação
Wallace, servidor público no Tribunal de Contas do Estado de São
Paulo (TCE SP), está implementando um sistema de controle de
acesso baseado em políticas. No Linux, o módulo que faz essa
implementação é o SELinux. Wallace está atribuindo rótulos de
segurança a cada processo e recursos do sistema. Após a
aplicação das diretrizes de segurança, Wallace elaborou um
relatório com o conteúdo das políticas implementadas e com
seus benefícios para a segurança do sistema operacional.
Dentre os benefícios, tem-se que:
Dentre os benefícios, tem-se que:
Q2322004
Segurança da Informação
Martin é um criptoanalista contratado pela empresa Z para
identificar o ataque ocorrido em sua rede. Após um tempo,
Martin tinha as seguintes informações:
- algoritmo de encriptação
- texto cifrado
- mensagem de texto claro escolhida pelo criptoanalista, com seu respectivo texto cifrado produzido pela chave secreta
- texto cifrado escolhido pelo criptoanalista, com seu respectivo texto claro decriptado produzido pela chave secreta
Com base nas informações obtidas pelo criptoanalista, pode-se identificar o ataque como:
- algoritmo de encriptação
- texto cifrado
- mensagem de texto claro escolhida pelo criptoanalista, com seu respectivo texto cifrado produzido pela chave secreta
- texto cifrado escolhido pelo criptoanalista, com seu respectivo texto claro decriptado produzido pela chave secreta
Com base nas informações obtidas pelo criptoanalista, pode-se identificar o ataque como:
Q2322003
Noções de Informática
Durante uma verificação de rotina, a Divisão de Segurança da
Informação do Tribunal de Contas do Estado de São Paulo (TCE
SP) identificou um ataque por vírus e que este estava ocorrendo e
efetuando o apagamento de arquivos no servidor. Em virtude da
forma de backup utilizada, notou-se também que os mesmos
arquivos também eram apagados no backup. Logo, o TCE SP
decidiu trocar o tipo de backup usado pelo backup em nuvem. O
tipo de backup executado pelo TCE SP, antes da implementação
do backup em nuvem, é o backup:
Q2322002
Inglês
Texto associado
Is It Live, or Is It Deepfake?
It’s been four decades since society was in awe of the quality of
recordings available from a cassette recorder tape. Today we
have something new to be in awe of: deepfakes. Deepfakes
include hyperrealistic videos that use artificial intelligence (AI) to
create fake digital content that looks and sounds real. The word is
a portmanteau of “deep learning” and “fake.” Deepfakes are
everywhere: from TV news to advertising, from national election
campaigns to wars between states, and from cybercriminals’
phishing campaigns to insurance claims that fraudsters file. And
deepfakes come in all shapes and sizes — videos, pictures, audio,
text, and any other digital material that can be manipulated with
AI. One estimate suggests that deepfake content online is
growing at the rate of 400% annually.
There appear to be legitimate uses of deepfakes, such as in the
medical industry to improve the diagnostic accuracy of AI
algorithms in identifying periodontal disease or to help medical
professionals create artificial patients (from real patient data) to
safely test new diagnoses and treatments or help physicians
make medical decisions. Deepfakes are also used to entertain, as
seen recently on America’s Got Talent, and there may be future
uses where deepfake could help teachers address the personal
needs and preferences of specific students.
Unfortunately, there is also the obvious downside, where the
most visible examples represent malicious and illegitimate uses.
Examples already exist.
Deepfakes also involve voice phishing, also known as vishing,
which has been among the most common techniques for
cybercriminals. This technique involves using cloned voices over
the phone to exploit the victim’s professional or personal
relationships by impersonating trusted individuals. In March
2019, cybercriminals were able to use a deepfake to fool the CEO
of a U.K.-based energy firm into making a US$234,000 wire
transfer. The British CEO who was victimized thought that the
person speaking on the phone was the chief executive of the
firm’s German parent company. The deepfake caller asked him to
transfer the funds to a Hungarian supplier within an hour,
emphasizing that the matter was extremely urgent. The
fraudsters used AI-based software to successfully imitate the
German executive’s voice. […]
What can be done to combat deepfakes? Could we create
deepfake detectors? Or create laws or a code of conduct that
probably would be ignored?
There are tools that can analyze the blood flow in a subject’s face
and then compare it to human blood flow activity to detect a
fake. Also, the European Union is working on addressing
manipulative behaviors.
There are downsides to both categories of solutions, but clearly
something needs to be done to build trust in this emerging and
disruptive technology. The problem isn’t going away. It is only
increasing.
Authors
Nit Kshetri, Bryan School of Business and Economics, University of
North Carolina at Greensboro, Greensboro, NC, USA
Joanna F. DeFranco, Software Engineering, The Pennsylvania
State University, Malvern, PA, USA
Jeffrey Voas, NIST, USA
Adapted from:
https://www.computer.org/csdl/magazine/co/2023/07/10154234/
1O1wTOn6ynC
The aim of the last paragraph is to:
Q2322001
Inglês
Texto associado
Is It Live, or Is It Deepfake?
It’s been four decades since society was in awe of the quality of
recordings available from a cassette recorder tape. Today we
have something new to be in awe of: deepfakes. Deepfakes
include hyperrealistic videos that use artificial intelligence (AI) to
create fake digital content that looks and sounds real. The word is
a portmanteau of “deep learning” and “fake.” Deepfakes are
everywhere: from TV news to advertising, from national election
campaigns to wars between states, and from cybercriminals’
phishing campaigns to insurance claims that fraudsters file. And
deepfakes come in all shapes and sizes — videos, pictures, audio,
text, and any other digital material that can be manipulated with
AI. One estimate suggests that deepfake content online is
growing at the rate of 400% annually.
There appear to be legitimate uses of deepfakes, such as in the
medical industry to improve the diagnostic accuracy of AI
algorithms in identifying periodontal disease or to help medical
professionals create artificial patients (from real patient data) to
safely test new diagnoses and treatments or help physicians
make medical decisions. Deepfakes are also used to entertain, as
seen recently on America’s Got Talent, and there may be future
uses where deepfake could help teachers address the personal
needs and preferences of specific students.
Unfortunately, there is also the obvious downside, where the
most visible examples represent malicious and illegitimate uses.
Examples already exist.
Deepfakes also involve voice phishing, also known as vishing,
which has been among the most common techniques for
cybercriminals. This technique involves using cloned voices over
the phone to exploit the victim’s professional or personal
relationships by impersonating trusted individuals. In March
2019, cybercriminals were able to use a deepfake to fool the CEO
of a U.K.-based energy firm into making a US$234,000 wire
transfer. The British CEO who was victimized thought that the
person speaking on the phone was the chief executive of the
firm’s German parent company. The deepfake caller asked him to
transfer the funds to a Hungarian supplier within an hour,
emphasizing that the matter was extremely urgent. The
fraudsters used AI-based software to successfully imitate the
German executive’s voice. […]
What can be done to combat deepfakes? Could we create
deepfake detectors? Or create laws or a code of conduct that
probably would be ignored?
There are tools that can analyze the blood flow in a subject’s face
and then compare it to human blood flow activity to detect a
fake. Also, the European Union is working on addressing
manipulative behaviors.
There are downsides to both categories of solutions, but clearly
something needs to be done to build trust in this emerging and
disruptive technology. The problem isn’t going away. It is only
increasing.
Authors
Nit Kshetri, Bryan School of Business and Economics, University of
North Carolina at Greensboro, Greensboro, NC, USA
Joanna F. DeFranco, Software Engineering, The Pennsylvania
State University, Malvern, PA, USA
Jeffrey Voas, NIST, USA
Adapted from:
https://www.computer.org/csdl/magazine/co/2023/07/10154234/
1O1wTOn6ynC
The word “downsides” in “There are downsides to both
categories” (7th paragraph) means:
Q2322000
Inglês
Texto associado
Is It Live, or Is It Deepfake?
It’s been four decades since society was in awe of the quality of
recordings available from a cassette recorder tape. Today we
have something new to be in awe of: deepfakes. Deepfakes
include hyperrealistic videos that use artificial intelligence (AI) to
create fake digital content that looks and sounds real. The word is
a portmanteau of “deep learning” and “fake.” Deepfakes are
everywhere: from TV news to advertising, from national election
campaigns to wars between states, and from cybercriminals’
phishing campaigns to insurance claims that fraudsters file. And
deepfakes come in all shapes and sizes — videos, pictures, audio,
text, and any other digital material that can be manipulated with
AI. One estimate suggests that deepfake content online is
growing at the rate of 400% annually.
There appear to be legitimate uses of deepfakes, such as in the
medical industry to improve the diagnostic accuracy of AI
algorithms in identifying periodontal disease or to help medical
professionals create artificial patients (from real patient data) to
safely test new diagnoses and treatments or help physicians
make medical decisions. Deepfakes are also used to entertain, as
seen recently on America’s Got Talent, and there may be future
uses where deepfake could help teachers address the personal
needs and preferences of specific students.
Unfortunately, there is also the obvious downside, where the
most visible examples represent malicious and illegitimate uses.
Examples already exist.
Deepfakes also involve voice phishing, also known as vishing,
which has been among the most common techniques for
cybercriminals. This technique involves using cloned voices over
the phone to exploit the victim’s professional or personal
relationships by impersonating trusted individuals. In March
2019, cybercriminals were able to use a deepfake to fool the CEO
of a U.K.-based energy firm into making a US$234,000 wire
transfer. The British CEO who was victimized thought that the
person speaking on the phone was the chief executive of the
firm’s German parent company. The deepfake caller asked him to
transfer the funds to a Hungarian supplier within an hour,
emphasizing that the matter was extremely urgent. The
fraudsters used AI-based software to successfully imitate the
German executive’s voice. […]
What can be done to combat deepfakes? Could we create
deepfake detectors? Or create laws or a code of conduct that
probably would be ignored?
There are tools that can analyze the blood flow in a subject’s face
and then compare it to human blood flow activity to detect a
fake. Also, the European Union is working on addressing
manipulative behaviors.
There are downsides to both categories of solutions, but clearly
something needs to be done to build trust in this emerging and
disruptive technology. The problem isn’t going away. It is only
increasing.
Authors
Nit Kshetri, Bryan School of Business and Economics, University of
North Carolina at Greensboro, Greensboro, NC, USA
Joanna F. DeFranco, Software Engineering, The Pennsylvania
State University, Malvern, PA, USA
Jeffrey Voas, NIST, USA
Adapted from:
https://www.computer.org/csdl/magazine/co/2023/07/10154234/
1O1wTOn6ynC
In the question “Or create laws or a code of conduct that
probably would be ignored?” (5th paragraph), the authors imply
that these laws and code of conduct may be:
Q2321999
Inglês
Texto associado
Is It Live, or Is It Deepfake?
It’s been four decades since society was in awe of the quality of
recordings available from a cassette recorder tape. Today we
have something new to be in awe of: deepfakes. Deepfakes
include hyperrealistic videos that use artificial intelligence (AI) to
create fake digital content that looks and sounds real. The word is
a portmanteau of “deep learning” and “fake.” Deepfakes are
everywhere: from TV news to advertising, from national election
campaigns to wars between states, and from cybercriminals’
phishing campaigns to insurance claims that fraudsters file. And
deepfakes come in all shapes and sizes — videos, pictures, audio,
text, and any other digital material that can be manipulated with
AI. One estimate suggests that deepfake content online is
growing at the rate of 400% annually.
There appear to be legitimate uses of deepfakes, such as in the
medical industry to improve the diagnostic accuracy of AI
algorithms in identifying periodontal disease or to help medical
professionals create artificial patients (from real patient data) to
safely test new diagnoses and treatments or help physicians
make medical decisions. Deepfakes are also used to entertain, as
seen recently on America’s Got Talent, and there may be future
uses where deepfake could help teachers address the personal
needs and preferences of specific students.
Unfortunately, there is also the obvious downside, where the
most visible examples represent malicious and illegitimate uses.
Examples already exist.
Deepfakes also involve voice phishing, also known as vishing,
which has been among the most common techniques for
cybercriminals. This technique involves using cloned voices over
the phone to exploit the victim’s professional or personal
relationships by impersonating trusted individuals. In March
2019, cybercriminals were able to use a deepfake to fool the CEO
of a U.K.-based energy firm into making a US$234,000 wire
transfer. The British CEO who was victimized thought that the
person speaking on the phone was the chief executive of the
firm’s German parent company. The deepfake caller asked him to
transfer the funds to a Hungarian supplier within an hour,
emphasizing that the matter was extremely urgent. The
fraudsters used AI-based software to successfully imitate the
German executive’s voice. […]
What can be done to combat deepfakes? Could we create
deepfake detectors? Or create laws or a code of conduct that
probably would be ignored?
There are tools that can analyze the blood flow in a subject’s face
and then compare it to human blood flow activity to detect a
fake. Also, the European Union is working on addressing
manipulative behaviors.
There are downsides to both categories of solutions, but clearly
something needs to be done to build trust in this emerging and
disruptive technology. The problem isn’t going away. It is only
increasing.
Authors
Nit Kshetri, Bryan School of Business and Economics, University of
North Carolina at Greensboro, Greensboro, NC, USA
Joanna F. DeFranco, Software Engineering, The Pennsylvania
State University, Malvern, PA, USA
Jeffrey Voas, NIST, USA
Adapted from:
https://www.computer.org/csdl/magazine/co/2023/07/10154234/
1O1wTOn6ynC
When the authors refer to the use of deepfake in education (2nd
paragraph), they state that ultimately teachers may find it:
Q2321998
Inglês
Texto associado
Is It Live, or Is It Deepfake?
It’s been four decades since society was in awe of the quality of
recordings available from a cassette recorder tape. Today we
have something new to be in awe of: deepfakes. Deepfakes
include hyperrealistic videos that use artificial intelligence (AI) to
create fake digital content that looks and sounds real. The word is
a portmanteau of “deep learning” and “fake.” Deepfakes are
everywhere: from TV news to advertising, from national election
campaigns to wars between states, and from cybercriminals’
phishing campaigns to insurance claims that fraudsters file. And
deepfakes come in all shapes and sizes — videos, pictures, audio,
text, and any other digital material that can be manipulated with
AI. One estimate suggests that deepfake content online is
growing at the rate of 400% annually.
There appear to be legitimate uses of deepfakes, such as in the
medical industry to improve the diagnostic accuracy of AI
algorithms in identifying periodontal disease or to help medical
professionals create artificial patients (from real patient data) to
safely test new diagnoses and treatments or help physicians
make medical decisions. Deepfakes are also used to entertain, as
seen recently on America’s Got Talent, and there may be future
uses where deepfake could help teachers address the personal
needs and preferences of specific students.
Unfortunately, there is also the obvious downside, where the
most visible examples represent malicious and illegitimate uses.
Examples already exist.
Deepfakes also involve voice phishing, also known as vishing,
which has been among the most common techniques for
cybercriminals. This technique involves using cloned voices over
the phone to exploit the victim’s professional or personal
relationships by impersonating trusted individuals. In March
2019, cybercriminals were able to use a deepfake to fool the CEO
of a U.K.-based energy firm into making a US$234,000 wire
transfer. The British CEO who was victimized thought that the
person speaking on the phone was the chief executive of the
firm’s German parent company. The deepfake caller asked him to
transfer the funds to a Hungarian supplier within an hour,
emphasizing that the matter was extremely urgent. The
fraudsters used AI-based software to successfully imitate the
German executive’s voice. […]
What can be done to combat deepfakes? Could we create
deepfake detectors? Or create laws or a code of conduct that
probably would be ignored?
There are tools that can analyze the blood flow in a subject’s face
and then compare it to human blood flow activity to detect a
fake. Also, the European Union is working on addressing
manipulative behaviors.
There are downsides to both categories of solutions, but clearly
something needs to be done to build trust in this emerging and
disruptive technology. The problem isn’t going away. It is only
increasing.
Authors
Nit Kshetri, Bryan School of Business and Economics, University of
North Carolina at Greensboro, Greensboro, NC, USA
Joanna F. DeFranco, Software Engineering, The Pennsylvania
State University, Malvern, PA, USA
Jeffrey Voas, NIST, USA
Adapted from:
https://www.computer.org/csdl/magazine/co/2023/07/10154234/
1O1wTOn6ynC
In the 1st sentence (“It’s been four decades since society was in
awe of the quality of recordings available from a cassette recorder
tape”), the reaction of society is described as being one of:
Q2321997
Inglês
Texto associado
Is It Live, or Is It Deepfake?
It’s been four decades since society was in awe of the quality of
recordings available from a cassette recorder tape. Today we
have something new to be in awe of: deepfakes. Deepfakes
include hyperrealistic videos that use artificial intelligence (AI) to
create fake digital content that looks and sounds real. The word is
a portmanteau of “deep learning” and “fake.” Deepfakes are
everywhere: from TV news to advertising, from national election
campaigns to wars between states, and from cybercriminals’
phishing campaigns to insurance claims that fraudsters file. And
deepfakes come in all shapes and sizes — videos, pictures, audio,
text, and any other digital material that can be manipulated with
AI. One estimate suggests that deepfake content online is
growing at the rate of 400% annually.
There appear to be legitimate uses of deepfakes, such as in the
medical industry to improve the diagnostic accuracy of AI
algorithms in identifying periodontal disease or to help medical
professionals create artificial patients (from real patient data) to
safely test new diagnoses and treatments or help physicians
make medical decisions. Deepfakes are also used to entertain, as
seen recently on America’s Got Talent, and there may be future
uses where deepfake could help teachers address the personal
needs and preferences of specific students.
Unfortunately, there is also the obvious downside, where the
most visible examples represent malicious and illegitimate uses.
Examples already exist.
Deepfakes also involve voice phishing, also known as vishing,
which has been among the most common techniques for
cybercriminals. This technique involves using cloned voices over
the phone to exploit the victim’s professional or personal
relationships by impersonating trusted individuals. In March
2019, cybercriminals were able to use a deepfake to fool the CEO
of a U.K.-based energy firm into making a US$234,000 wire
transfer. The British CEO who was victimized thought that the
person speaking on the phone was the chief executive of the
firm’s German parent company. The deepfake caller asked him to
transfer the funds to a Hungarian supplier within an hour,
emphasizing that the matter was extremely urgent. The
fraudsters used AI-based software to successfully imitate the
German executive’s voice. […]
What can be done to combat deepfakes? Could we create
deepfake detectors? Or create laws or a code of conduct that
probably would be ignored?
There are tools that can analyze the blood flow in a subject’s face
and then compare it to human blood flow activity to detect a
fake. Also, the European Union is working on addressing
manipulative behaviors.
There are downsides to both categories of solutions, but clearly
something needs to be done to build trust in this emerging and
disruptive technology. The problem isn’t going away. It is only
increasing.
Authors
Nit Kshetri, Bryan School of Business and Economics, University of
North Carolina at Greensboro, Greensboro, NC, USA
Joanna F. DeFranco, Software Engineering, The Pennsylvania
State University, Malvern, PA, USA
Jeffrey Voas, NIST, USA
Adapted from:
https://www.computer.org/csdl/magazine/co/2023/07/10154234/
1O1wTOn6ynC
Based on the text, mark the statements below as true (T) or false (F).
( ) Deepfakes are circumscribed to certain areas of action.
( ) The sole aim of deepfake technology is to spread misinformation.
( ) Evidence shows that even high-ranking executives can be easy targets to vishing techniques.
The statements are, respectively:
( ) Deepfakes are circumscribed to certain areas of action.
( ) The sole aim of deepfake technology is to spread misinformation.
( ) Evidence shows that even high-ranking executives can be easy targets to vishing techniques.
The statements are, respectively:
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