> For the complete documentation index, see [llms.txt](https://writeups.ayweth20.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://writeups.ayweth20.com/2025/404ctf-2025/securite-materielle/trop-diq.md).

# Trop d'IQ

**Catégorie:** Sécurité matérielle - **Difficulté:** Intro

{% file src="/files/EXxW52DS6QZx5CCU5BJX" %}

**Description:**

<figure><img src="/files/TkMLXSP7nxm2QiHCxdsj" alt=""><figcaption></figcaption></figure>

Solution:

Pour ce challenge d'intro, il fallait réussir à convertir ce fichier .iq (signal audio) en un audio compréhensible par l'oreille humaine.

En sachant cela, j'ai créé un script qui fait tout ça automatiquement :&#x20;

```python
import numpy as np
from scipy.io.wavfile import write

# Paramètres
input_file = "chall.iq"
output_file = "output.wav"
sample_rate = 44100  # Hz

# Lire IQ comme complex128 (résultat d'une FFT)
fft_data = np.fromfile(input_file, dtype=np.complex128)

print(f"[+] Chargé {len(fft_data)} coefficients FFT depuis '{input_file}'")

# Revenir au domaine temporel
time_data = np.fft.ifft(fft_data)

# Garder uniquement la partie réelle
audio_signal = np.real(time_data)

# Normaliser entre -1 et 1
audio_signal /= np.max(np.abs(audio_signal))

# Convertir en float32
audio_signal = audio_signal.astype(np.float32)

# Sauvegarder le son
write(output_file, sample_rate, audio_signal)

print(f"[+] Signal reconstruit et sauvegardé dans '{output_file}'")
```

Et en sortie, nous avons un bel audio bien propre que nous pouvons écouter :&#x20;

{% file src="/files/qJkET02GwUJlOthQ7PGZ" %}

<details>

<summary>🚩FLAG</summary>

`404CTF{45d05a87}`

</details>


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