Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/rigged-poker-games.html
The Department of Justice has indicted thirty-one people over the high-tech rigging of high-stakes poker games.
In a typical legitimate poker game, a dealer uses a shuffling machine to shuffle the cards randomly before dealing them to all the players in a particular order. As set forth in the indictment, the rigged games used altered shuffling machines that contained hidden technology allowing the machines to read all the cards in the deck. Because the cards were always dealt in a particular order to the players at the table, the machines could determine which player would have the winning hand. This information was transmitted to an off-site member of the conspiracy, who then transmitted that information via cellphone back to a member of the conspiracy who was playing at the table, referred to as the “Quarterback” or “Driver.” The Quarterback then secretly signaled this information (usually by prearranged signals like touching certain chips or other items on the table) to other co-conspirators playing at the table, who were also participants in the scheme. Collectively, the Quarterback and other players in on the scheme (i.e., the cheating team) used this information to win poker games against unwitting victims, who sometimes lost tens or hundreds of thousands of dollars at a time. The defendants used other cheating technology as well, such as a chip tray analyzer (essentially, a poker chip tray that also secretly read all cards using hidden cameras), an x-ray table that could read cards face down on the table, and special contact lenses or eyeglasses that could read pre-marked cards.
News articles.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/12/casino-players-using-hidden-cameras-for-cheating.html
The basic strategy is to place a device with a hidden camera in a position to capture normally hidden card values, which are interpreted by an accomplice off-site and fed back to the player via a hidden microphone. Miniaturization is making these devices harder to detect. Presumably AI will soon obviate the need for an accomplice.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/10/coin-flips-are-biased.html
Experimental result:
Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected 350,757 coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Persi Diaconis. The model asserts that when people flip an ordinary coin, it tends to land on the same side it started—Diaconis estimated the probability of a same-side outcome to be about 51%.
And the final paragraph:
Could future coin tossers use the same-side bias to their advantage? The magnitude of the observed bias can be illustrated using a betting scenario. If you bet a dollar on the outcome of a coin toss (i.e., paying 1 dollar to enter, and winning either 0 or 2 dollars depending on the outcome) and repeat the bet 1,000 times, knowing the starting position of the coin toss would earn you 19 dollars on average. This is more than the casino advantage for 6 deck blackjack against an optimal-strategy player, where the casino would make 5 dollars on a comparable bet, but less than the casino advantage for single-zero roulette, where the casino would make 27 dollars on average. These considerations lead us to suggest that when coin flips are used for high-stakes decision-making, the starting position of the coin is best concealed.
Boing Boing post.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/04/gaining-an-advantage-in-roulette.html
You can beat the game without a computer:
On a perfect [roulette] wheel, the ball would always fall in a random way. But over time, wheels develop flaws, which turn into patterns. A wheel that’s even marginally tilted could develop what Barnett called a ‘drop zone.’ When the tilt forces the ball to climb a slope, the ball decelerates and falls from the outer rim at the same spot on almost every spin. A similar thing can happen on equipment worn from repeated use, or if a croupier’s hand lotion has left residue, or for a dizzying number of other reasons. A drop zone is the Achilles’ heel of roulette. That morsel of predictability is enough for software to overcome the random skidding and bouncing that happens after the drop.”
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/10/on-the-randomness-of-automatic-card-shufflers.html
Many years ago, Matt Blaze and I talked about getting our hands on a casino-grade automatic shuffler and looking for vulnerabilities. We never did it—I remember that we didn’t even try very hard—but this article shows that we probably would have found non-random properties:
…the executives had recently discovered that one of their machines had been hacked by a gang of hustlers. The gang used a hidden video camera to record the workings of the card shuffler through a glass window. The images, transmitted to an accomplice outside in the casino parking lot, were played back in slow motion to figure out the sequence of cards in the deck, which was then communicated back to the gamblers inside. The casino lost millions of dollars before the gang were finally caught.
Stanford mathematician Persi Diaconis found other flaws:
With his collaborator Susan Holmes, a statistician at Stanford, Diaconis travelled to the company’s Las Vegas showroom to examine a prototype of their new machine. The pair soon discovered a flaw. Although the mechanical shuffling action appeared random, the mathematicians noticed that the resulting deck still had rising and falling sequences, which meant that they could make predictions about the card order.
New Scientist article behind a paywall. Slashdot thread.
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