Juan De Dios (@Shankatorium) header
Juan De Dios (@Shankatorium) profile picture
Philippines
Joined May 2011
Photo by Juan De Dios (@Shankatorium)Photo by Juan De Dios (@Shankatorium)
Juan De Dios retweeted
Яркий пример того, почему Desert Eagle не используется для самозащиты: его отдача. Оружие практически невозможно контролировать при стрельбе.
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Juan De Dios retweeted
The national Social Weather Survey of June 20-29, 2026 found that 26% (est. 7.3 million) of Philippine households have at least one member who uses a bicycle. Why then is the budget for active transport so minuscule? Photo by Oscar Nava. manilatimes.net/2026/10/31/b…
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Juan De Dios retweeted
So what kind of laptop do you have?
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"White man has been here" "How can you tell?" "Drunk thanksgiving incest fantasy"
POV: How your aunt looks at you after her third divorce and her fifth shot of Thanksgiving bourbon.
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Juan De Dios retweeted
Never forget during Bruno he was walking around the Middle East tauting Arabs to attack him but they all were kind to him and the people that did attack him were Israelis and he was forced to break character and remove it from the movie.
Me movie comin to Prime Video dis Wendesday, it 89 minute long, dere’s 14400 minutes in a day meanin u’ll be able to watch it 161 times a day for de rest of ya lives for free
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Juan De Dios retweeted
The National Museum of American History has an exhibit right now on Filipino American history. It includes the suits that Filipino immigrants bought using the money they earned as agricultural laborers. Photos from Mina Roces's article "These Guys Came Out Looking Like Movie Actors': Filipino Dress and Consumer Practices in the United States, 1920s-1930s"
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Juan De Dios retweeted
This quoted post is unavailable.
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Juan De Dios retweeted
Ganito ka-brutal si Bato noong panahon ng War on Drugs.🔥🔥🔥
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Juan De Dios retweeted
Indonesia’s founding fathers considered Rizal as their own; they even had Ultimo Adios translated and utilized as a battle cry during their war for independence. Rizal’s legacy is truly shared throughout Southeast Asia!
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Even back then Filipinos were always fresh
How remarkable that an agricultural worker in the 1930s had access to better tailoring than the US Vice President today.
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Nigga ain't you dead too?
Rest in peace, Sir Patrick Stewart... you will be greatly missed by the world.
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Juan De Dios retweeted
“Yeah - I’m a big fan of Paddy the Pimblett” has me dying hahahaha Justin Gaethje was speechless.
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Juan De Dios retweeted
Team Khabib Positive Drug Test History
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Juan De Dios retweeted
This is an open secret in Congress. Pati sa Senate.
Upon being asked by Associate Justice Karl Miranda if all DPWH projects have proponents and kickbacks, former DPWH Usec. Roberto Bernardo answered in the affirmative. | via @KaxandraSalonga
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Juan De Dios retweeted
incredible tweet - way way way ahead of its time
Unlike most people on Twitter, I’m actually using GPT3 in a production system. The mistake people are making is they are asking “how can I use this to automate a smart person’s job”, when they should be asking “what would I do if I had unlimited dumb people”
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I wanted to bring this to your attention: @bitagbentulfo @erwintulforeal @RamonTulfoII
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Juan De Dios retweeted
ano po kapansanan nya?
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Juan De Dios retweeted
We all forgot about this way too quickly
Are any of the fights tomorrow rigged?
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Juan De Dios retweeted
You can point an ultrasonic speaker at an iPhone from a few feet away, play an audio command that no human ear can hear, and the phone will wake up, open Safari, and dial a phone number. To everyone standing in the room, it sounds like dead silence. To the phone, it sounds like someone speaking directly into the microphone. Mobile operating systems use digital low-pass filters to delete all sound above 20,000 Hz, assuming inaudible frequencies can never reach the voice assistant. Yet this attack succeeds without exploiting a single line of software code. The flaw is not in the operating system. It is in the microscopic silicon plate inside the microphone itself. 1. The microscopic machine inside your microphone Inside modern smartphones, microphones are not miniature versions of studio microphones. They are micro-electro-mechanical systems (MEMS) chips smaller than a grain of rice. Inside the chip package sits a flexible silicon diaphragm suspended barely two micrometers above a rigid metal backplate. The two plates form a variable capacitor. When sound waves hit the silicon diaphragm, it flexes inward and outward, changing the electrical capacitance. An onboard preamplifier converts that movement into an electrical voltage. At normal room volumes, the silicon plate moves evenly: double the sound pressure, and the plate flexes twice as far. 2. The non-linear spring catch The flaw appears when sound waves hit the microphone at high pressure (above 90 dB SPL). If you broadcast an ultrasound carrier wave (say, 28,000 Hz) that has an audio command modulated on top of it, the microphone front-end cannot move symmetrically. The mechanical spring resistance of the silicon, the electrical pull between the plates, and the preamplifier circuit all reach their limits. Instead of flexing smoothly, the silicon plate bends slightly more inward than outward. This asymmetry acts like an old crystal radio receiver. Mathematically, a non-linear response squares the incoming wave. That squaring action strips the silent 28,000 Hz carrier away and leaves behind the original human voice command in the audible 300 Hz to 3,500 Hz range. By the time the analog-to-digital converter reads the signal, the ultrasound is already gone. It has physically transformed into ordinary low-frequency sound inside the microphone chamber. The phone digital filter never triggers because the audio reaching the software is already normal human voice. 3. What researchers actually proved (DolphinAttack) In 2017, researchers at Zhejiang University published DolphinAttack, demonstrating this hardware flaw across Siri, Google Now, and Alexa. They successfully forced phones to initiate FaceTime calls, navigate to malicious websites, turn on airplane mode, and unlock smart doors paired to the device. Later research expanded this into attacking through solid tables, using the furniture itself as a physical acoustic conductor. 4. Why this fails in the wild (The physical friction) Demonstrating this in an acoustic laboratory is straightforward. Pulling it off in a crowded coffee shop runs into strict physical limits: First, air absorbs high-frequency sound rapidly. Bass travels through concrete walls, but 30,000 Hz ultrasound loses roughly 1 to 1.5 decibels of power per meter in normal room air. In the original DolphinAttack paper, the effective range was limited between a few centimeters and 1.75 meters. Reaching across an entire room requires a heavy array of high-voltage transducers. Second, ultrasonic emitters act like laser pointers. High-frequency sound travels in narrow beams of 15 to 20 degrees. If you fail to aim directly at the phone one-millimeter microphone port, sound pressure drops below the 90 dB threshold needed to bend the plate. Third, modern phones are waterproof. Devices with IP68 ratings place a hydrophobic Gore-Tex mesh over the microphone hole. The mesh lets air equalize while stopping water, but it acts like a dense acoustic muffler against ultrasonic frequencies, absorbing another 14 dB of signal strength. Finally, voice assistants use personal voice training. If Siri or Google Assistant is set to only respond to the device owner, a generic synthesized ultrasonic command is ignored unless the attacker models the owner vocal pitch. 5. How modern hardware catches it Defending against inaudible voice injection requires catching the acoustic mismatch before speech recognition executes: * Multi-microphone cross-checks: Modern smartphones carry three or four microphones positioned on the bottom, top, and camera bump. When a real human speaks, their voice reaches all microphones with natural arrival delays. A narrow ultrasonic beam hitting one microphone port creates an unnatural phase imbalance across the device. * Harmonic distortion analysis: When a microphone is driven into non-linear demodulation, it does not produce a clean voice recording. It leaves distinct distortion products and second-order harmonics. Software filters can analyze the spectrum and mute the assistant if they detect the signature of mechanical demodulation. * Acoustic damping barriers: Microphone manufacturers can install physical acoustic damping layers inside the chip housing that attenuate frequencies above 22,000 Hz before the sound wave ever reaches the silicon diaphragm. Inaudible voice injection works because digital security models assume analog sensors behave linearly. When sound waves push silicon past its linear limits, the hardware itself executes operations the operating system never authorized.
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Phreaking an iPhone
You can point an ultrasonic speaker at an iPhone from a few feet away, play an audio command that no human ear can hear, and the phone will wake up, open Safari, and dial a phone number. To everyone standing in the room, it sounds like dead silence. To the phone, it sounds like someone speaking directly into the microphone. Mobile operating systems use digital low-pass filters to delete all sound above 20,000 Hz, assuming inaudible frequencies can never reach the voice assistant. Yet this attack succeeds without exploiting a single line of software code. The flaw is not in the operating system. It is in the microscopic silicon plate inside the microphone itself. 1. The microscopic machine inside your microphone Inside modern smartphones, microphones are not miniature versions of studio microphones. They are micro-electro-mechanical systems (MEMS) chips smaller than a grain of rice. Inside the chip package sits a flexible silicon diaphragm suspended barely two micrometers above a rigid metal backplate. The two plates form a variable capacitor. When sound waves hit the silicon diaphragm, it flexes inward and outward, changing the electrical capacitance. An onboard preamplifier converts that movement into an electrical voltage. At normal room volumes, the silicon plate moves evenly: double the sound pressure, and the plate flexes twice as far. 2. The non-linear spring catch The flaw appears when sound waves hit the microphone at high pressure (above 90 dB SPL). If you broadcast an ultrasound carrier wave (say, 28,000 Hz) that has an audio command modulated on top of it, the microphone front-end cannot move symmetrically. The mechanical spring resistance of the silicon, the electrical pull between the plates, and the preamplifier circuit all reach their limits. Instead of flexing smoothly, the silicon plate bends slightly more inward than outward. This asymmetry acts like an old crystal radio receiver. Mathematically, a non-linear response squares the incoming wave. That squaring action strips the silent 28,000 Hz carrier away and leaves behind the original human voice command in the audible 300 Hz to 3,500 Hz range. By the time the analog-to-digital converter reads the signal, the ultrasound is already gone. It has physically transformed into ordinary low-frequency sound inside the microphone chamber. The phone digital filter never triggers because the audio reaching the software is already normal human voice. 3. What researchers actually proved (DolphinAttack) In 2017, researchers at Zhejiang University published DolphinAttack, demonstrating this hardware flaw across Siri, Google Now, and Alexa. They successfully forced phones to initiate FaceTime calls, navigate to malicious websites, turn on airplane mode, and unlock smart doors paired to the device. Later research expanded this into attacking through solid tables, using the furniture itself as a physical acoustic conductor. 4. Why this fails in the wild (The physical friction) Demonstrating this in an acoustic laboratory is straightforward. Pulling it off in a crowded coffee shop runs into strict physical limits: First, air absorbs high-frequency sound rapidly. Bass travels through concrete walls, but 30,000 Hz ultrasound loses roughly 1 to 1.5 decibels of power per meter in normal room air. In the original DolphinAttack paper, the effective range was limited between a few centimeters and 1.75 meters. Reaching across an entire room requires a heavy array of high-voltage transducers. Second, ultrasonic emitters act like laser pointers. High-frequency sound travels in narrow beams of 15 to 20 degrees. If you fail to aim directly at the phone one-millimeter microphone port, sound pressure drops below the 90 dB threshold needed to bend the plate. Third, modern phones are waterproof. Devices with IP68 ratings place a hydrophobic Gore-Tex mesh over the microphone hole. The mesh lets air equalize while stopping water, but it acts like a dense acoustic muffler against ultrasonic frequencies, absorbing another 14 dB of signal strength. Finally, voice assistants use personal voice training. If Siri or Google Assistant is set to only respond to the device owner, a generic synthesized ultrasonic command is ignored unless the attacker models the owner vocal pitch. 5. How modern hardware catches it Defending against inaudible voice injection requires catching the acoustic mismatch before speech recognition executes: * Multi-microphone cross-checks: Modern smartphones carry three or four microphones positioned on the bottom, top, and camera bump. When a real human speaks, their voice reaches all microphones with natural arrival delays. A narrow ultrasonic beam hitting one microphone port creates an unnatural phase imbalance across the device. * Harmonic distortion analysis: When a microphone is driven into non-linear demodulation, it does not produce a clean voice recording. It leaves distinct distortion products and second-order harmonics. Software filters can analyze the spectrum and mute the assistant if they detect the signature of mechanical demodulation. * Acoustic damping barriers: Microphone manufacturers can install physical acoustic damping layers inside the chip housing that attenuate frequencies above 22,000 Hz before the sound wave ever reaches the silicon diaphragm. Inaudible voice injection works because digital security models assume analog sensors behave linearly. When sound waves push silicon past its linear limits, the hardware itself executes operations the operating system never authorized.
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